Official specifications and benchmark inputs were verified August 7, 2026. This update reports a completed 16-cell consumer-web matrix and an isolated 55-request API run. Consumer-product and API results remain separate datasets with separate denominators and no cross-surface score.
Independent publication notice: Chat-Deep.ai is not owned, operated, endorsed by, or affiliated with DeepSeek or OpenAI. Product names and trademarks belong to their respective owners. This comparison contains no sponsored ranking and must not claim a universal winner.
DeepSeek vs ChatGPT: the quick answer
DeepSeek and ChatGPT overlap as AI assistants, but they are not interchangeable products. DeepSeek is compelling when low published API prices, compatible request formats, or self-managed model weights matter. ChatGPT is the broader managed workspace for people who want files, research, data analysis, images, voice, memory, projects, coding tools, and organizational controls in one product. The better choice depends on the exact surface, plan, model, workload, data rules, and total cost.
- Instant web lane: ChatGPT passed 3/4 cells strictly; DeepSeek passed 1/4. ChatGPT’s PDF-chart attempt was still answering at the fixed 60-second observation limit and remains a 0/3 failure.
- Reasoning web lane: ChatGPT High passed 4/4 scored cells. DeepSeek Expert passed 1/1 scored cell; two PDF cells and one citation cell were N/A because the checked interface exposed neither the required upload nor Search control.
- API quality lane: DeepSeek V4 Flash High passed 22/23 supported attempts; GPT-5.6 Terra High passed 32/32. DeepSeek also had nine declared N/A capability cells that were excluded rather than scored as failures.
- Short API streams: across five sequential runs, median TTFT was 994.016 ms for DeepSeek and 633.137 ms for OpenAI; median total time was 994.240 ms and 885.849 ms. The run region was not recorded, so this is not a geographic speed claim.
- Measured API spend: the matched 23-call core cost was $0.0500209248 for DeepSeek and $1.683402 for OpenAI. Full applicable spend was $0.0500209248 across 23 DeepSeek calls and $1.7867516 across 32 OpenAI calls; the full-run workloads are not equal.
Bottom line: these results identify task-level tradeoffs, not an overall winner. Product coverage, accuracy, latency, cost, and available controls must be evaluated separately.
| Decision | DeepSeek | ChatGPT or OpenAI | What must be tested |
|---|---|---|---|
| Everyday assistant | Focused chat experience with Instant and Expert modes | Broader managed workspace; features and limits depend on plan and mode | Same fresh-chat prompts in visible UI modes |
| Cost-sensitive API | Very low published V4 token prices | Luna, Terra, and Sol cover different cost and capability tiers | Cost per accepted result, not price alone |
| Files and research | Consumer file and search controls; documented V4 API remains a separate surface | Consumer tools plus file and web-search paths in the OpenAI platform | Native ingestion, answer-key accuracy, and citation support |
| Infrastructure control | Published V4 weights create a self-managed route | ChatGPT and the compared GPT API models are managed services | Security, hardware, operations, and total ownership cost |
| Quality winner | No universal winner can be established from specifications | Identical prompts, declared settings, multiple repeats, and written rubrics | |
Use the DeepSeek comparisons hub for other providers. The rest of this page explains the evidence boundary first, then presents official facts, original test results, pricing, privacy, and workload-specific decision rules.
Chat products, APIs, and self-hosting are different comparisons
A consumer chat plan buys an interface, product tools, account limits, and provider-managed routing. An API buys metered requests to a named model and exposes settings, usage, finish status, and documented tools. Self-hosting transfers infrastructure, security, scaling, monitoring, and upgrade responsibility to the operator. A result from one surface must not be presented as a result from another.
- Web-app evidence must record the plan, visible mode, search state, file state, test date, and any visible fallback. A UI label is not proof of an API model ID.
- API evidence must record the exact model ID, endpoint, reasoning or thinking setting, output ceiling, service tier, usage, timing, errors, and cost formula.
- Self-hosted evidence would need the exact checkpoint, license, quantization, hardware, serving engine, precision, prompt template, context implementation, and decoding settings. It is outside the hosted comparison unless tested separately.
This separation prevents three common mistakes: treating a ChatGPT subscription as API credit, assuming a DeepSeek chat mode is the same as a hosted API ID, and comparing a token rate with the full cost of operating model weights.
Current official model facts
| Item | DeepSeek V4 API | OpenAI GPT-5.6 API |
|---|---|---|
| Current compared IDs | deepseek-v4-flash (V4-Flash-0731 public beta) and deepseek-v4-pro (V4-Pro-0813 GA) | gpt-5.6-luna, gpt-5.6-terra, and gpt-5.6-sol |
| Documented context | 1,000,000 tokens | 1,050,000 tokens |
| Documented maximum output | 384,000 tokens | 128,000 tokens |
| Reasoning control | Thinking defaults on; low → Low; medium/high/xhigh → High; max → Max | Reasoning effort supports none, low, medium, high, xhigh, and max on the checked models |
| Structured output | Chat: json_object; Responses: text.format with json_schema | Structured Outputs with JSON Schema are documented |
| Hosted web search | Responses: server-side web_search; Chat Completions: no hosted search tool | Server-side web search is documented on eligible Responses models |
| Input modality in the checked model pages | Text-oriented model API; consumer file controls are separate | Text and image input, text output |
| Responses API | Documented for both Flash and Pro; stateless | Listed for Luna, Terra, and Sol |
Context-window size is a capacity specification, not an accuracy score. A model may accept a long request and still miss, distort, or underuse information. See the independent DeepSeek 1M context benchmark for a separate retrieval study, and verify the current model list in the DeepSeek V4 model guide.
DeepSeek Chat vs ChatGPT: web-app tests
The consumer lane measures the products people actually use in a browser. It does not report API token prices, API context windows, or API model IDs as UI results. The available ChatGPT Pro account permits paid-mode testing, but the record must show the exact visible mode and any fallback. DeepSeek must be recorded the same way.
UI test method and run record
- Use new chats, the same English prompt, and the same synthetic fixture for both products.
- Record account plan, visible mode, search state, upload state, region, date, and any fallback or limit message.
- Score exact fields with a frozen answer key; retain type errors, formatting errors, refusals, and incomplete answers.
- Do not report exact UI latency unless a reliable completion event and repeat protocol are captured.
- Do not report UI token counts or cost unless the product exposes those values directly.
| Product and plan | Visible mode | Search state | File state | Coverage |
|---|---|---|---|---|
| ChatGPT web, Pro | Instant | On only for citation cell | Attached for two PDF cells | 4/4 scored |
| ChatGPT web, Pro | High | On only for citation cell | Attached for two PDF cells | 4/4 scored |
| DeepSeek Chat, web account; no paid tier asserted | Instant | On only for citation cell | Attached for two PDF cells | 4/4 scored |
| DeepSeek Chat, web account; no paid tier asserted | Expert | No Search control for citation cell | No upload control for two PDF cells | 1/1 scored; 3 N/A |
All 16 cells used fresh chats on August 7, 2026. Region was not measured and no fallback was observed. Visible UI labels are reported as product labels, not inferred API model IDs; UI token counts, per-response cost, and general latency were not estimated.
Instant-mode result
The fast lane pairs the visible DeepSeek Instant route with ChatGPT Instant. The result table names the exact visible labels and scores each task independently. It does not imply that either UI route is identical to deepseek-v4-flash or gpt-5.6-luna.
| Case | ChatGPT Instant | DeepSeek Instant |
|---|---|---|
| Instruction stack | 12/12, strict pass | 9/12, fail |
| Native PDF table | 3/3, strict pass | 2/3, fail |
| Native PDF chart | 0/3, timeout retained as failure | 1/3, fail |
| Official-web citations | 10/10, strict pass | 10/10, strict pass |
| Strict cells | 3/4 | 1/4 |
ChatGPT Instant’s PDF-chart response was still generating at the fixed 60-second observation limit and is retained as 0/3, not silently excluded. DeepSeek’s instruction response missed three exact-format checks. This eight-attempt lane supports only these four-task findings.
Reasoning-mode result
The reasoning lane pairs DeepSeek Expert with ChatGPT High. Any automatic fallback changes the evaluation cell and is disclosed or excluded under the declared protocol. The result names the visible responding route rather than inferring a hidden API model.
| Case | ChatGPT High | DeepSeek Expert |
|---|---|---|
| Instruction stack | 12/12, strict pass | 12/12, strict pass |
| Native PDF table | 3/3, strict pass | N/A: no file-upload control |
| Native PDF chart | 3/3, strict pass | N/A: no file-upload control |
| Official-web citations | 10/10, strict pass | N/A: no Search control |
| Strict scored cells | 4/4 | 1/1; 3 N/A |
No fallback was observed. The unequal 4-versus-1 quality denominators make a complete reasoning-lane winner claim invalid. ChatGPT High’s citation record passed two-reviewer adjudication, but no publication screenshot was retained for that cell.
Native PDF result
The PDF lane uses the same synthetic document and exact typed-field prompts in both web apps. Scoring separates file acceptance from answer accuracy. Each published screenshot shows the complete output and every field used by the rubric; cropped values are not acceptable evidence. Broader PDF intent belongs in the dedicated DeepSeek PDF and file analysis benchmark.
| Mode and case | Score | Observed error or status |
|---|---|---|
| ChatGPT Instant, table | 3/3 | Strict pass |
| DeepSeek Instant, table | 2/3 | NW-Q2-731 became NW- Q2- 731 |
| ChatGPT High, table | 3/3 | Strict pass |
| DeepSeek Expert, table | N/A | No file-upload control |
| ChatGPT Instant, chart | 0/3 | Still answering at 60 seconds; retained failure |
| DeepSeek Instant, chart | 1/3 | South correct; value 22 instead of 52; gap 1 instead of 5 |
| ChatGPT High, chart | 3/3 | Strict pass |
| DeepSeek Expert, chart | N/A | No file-upload control |
The same PDF, SHA-256 cb3259211734feda0265ecf46cc76745e116d72782b01e5e3c136d9fa7b0a07f, was used throughout. Six cells were scored and two were N/A; the table task produced 2/3 strict cells and the chart task 1/3. No output-type error was recorded. The figures below show the complete Instant table outputs; the High and chart cells are retained in the sanitized scoring record but are not illustrated here.
Web search and citation result
The citation lane uses the same dated question with search enabled. A link is not automatically a valid citation: adjudication verifies that it loads, is the requested source type, and directly supports the attached claim. The result discloses the question count, attempt count, adjudicator count, source-domain rule, and strict-pass denominator.
| Mode | Claims supported | Valid official URLs | Rubric score | Review |
|---|---|---|---|---|
| ChatGPT Instant | 2/2 | 2/2 | 10/10, strict pass | 2/2 reviewers |
| ChatGPT High | 2/2 | 2/2 | 10/10, strict pass | 2/2 reviewers |
| DeepSeek Instant | 2/2 | 3/3 URL occurrences; two canonical destinations | 10/10, strict pass | 2/2 reviewers |
| DeepSeek Expert | N/A | N/A | N/A: no Search control | Excluded |
Both blinded reviewers agreed on every rubric dimension for all three scored attempts: factual accuracy, official domain, direct support, inline placement, and no unsupported extra claim. Tracking parameters were removed for canonical comparison while the actual destination host was checked. The strict result is 3/3 scored cells; Expert is N/A, not a failure. Only the two Instant records have publication screenshots.
DeepSeek API vs OpenAI API: matched tests
The API lane is a separate model-endpoint comparison. The tested pair is deepseek-v4-flash with thinking enabled at High effort versus gpt-5.6-terra at High effort. This is an editorial pairing, not a provider claim of equivalence. Pro and Sol remain in the dated specification and price tables as higher tiers, not as a second measured matrix.
API method and run record
- Use frozen prompts, synthetic fixtures, answer keys, graders, provider order, and exclusion codes.
- Run three valid repeats for each quality task and five sequential requests for the short-latency task unless the protocol declares another count.
- Keep concurrency at one and record the run environment and region.
- Retain refusals, malformed output, tool failures, accepted-request timeouts, rate limits, truncation, and content filters as outcomes.
- Exclude only predeclared fixture, request-builder, fallback, corrupt-upload, or pre-output provider errors, with the reason visible.
- Calculate cost from provider-reported usage and the dated rate table. Subscription prices are not API costs.
| Run field | DeepSeek | OpenAI |
|---|---|---|
| Requested and returned model | deepseek-v4-flash | gpt-5.6-terra |
| Endpoint | /chat/completions | /v1/responses |
| Reasoning setting | Thinking enabled; High effort | Standard reasoning mode; High effort |
| Output ceilings | 16,384 / 32,768 / 65,536 by case | 16,384 / 32,768 / 65,536 by case |
| Service tier | Not returned | default |
| Version evidence | fp_a18b46594c_prod0820_fp8_kvcache_20260402 on 18 non-stream responses; absent on five streams | No system fingerprint returned |
| Fallback | None observed | None observed |
The executor version was 1.0.4, concurrency was one, and provider order rotated under the frozen plan. All 55 paid attempts completed successfully with zero exclusions: 23 DeepSeek and 32 OpenAI. Nine additional DeepSeek capability cells were declared N/A before execution. Region was not recorded, so the API timings do not support a location-specific claim.
Balanced API result: Flash vs Terra
The result table covers code debugging, normalized document extraction, structured output, strict instruction following, target-64K and target-256K retrieval, and declared capability-specific cells. Every score includes its numerator and denominator. A feature that is not applicable in a checked API is an N/A capability cell, not a failed quality attempt.
| API task | DeepSeek V4 Flash High | GPT-5.6 Terra High |
|---|---|---|
| Code diagnosis | 2/3 strict; 14/15 assertions | 3/3 strict; 15/15 assertions |
| Normalized document | 3/3 strict; 9/9 assertions | 3/3 strict; 9/9 assertions |
| Native PDF table | N/A | 3/3 strict; 9/9 assertions |
| Native PDF chart | N/A | 3/3 strict; 9/9 assertions |
| Target-64K retrieval | 3/3 strict; 12/12 assertions | 3/3 strict; 12/12 assertions |
| Target-256K retrieval | 3/3 strict; 12/12 assertions | 3/3 strict; 12/12 assertions |
| Official-web citations | N/A | 3/3 strict; 30/30 points |
| Structured JSON | 3/3 strict; 15/15 assertions | 3/3 strict; 15/15 assertions |
| Instruction stack | 3/3 strict; 36/36 assertions | 3/3 strict; 36/36 assertions |
| Exact short reply | 5/5 | 5/5 |
| Supported-attempt total | 22/23 | 32/32 |
There were zero exclusions. DeepSeek’s nine N/A cells were three repeats each for native PDF table, native PDF chart, and provider-hosted official-web citations. Both models passed all six long-context retrieval attempts, but the provider-reported input counts differed: 52,566-52,567 versus 55,372-55,373 at target-64K, and 209,507-209,510 versus 221,094-221,097 at target-256K. Target labels are protocol tiers, not identical token counts. The unequal supported-task coverage prevents an overall API winner claim.
API latency, tokens, and cost per run
Latency reporting must distinguish time to first visible text from total response time. Show individual observations plus the median, not a single best run. Cost must use input, cached-input, output, and tool usage reported by each provider, with the applicable long-context multiplier. A lower request cost is not proof of lower production cost if validation, retries, or missing tools add work.
| Provider | TTFT observations, ms | Median TTFT | Total-time observations, ms | Median total | Short-run cost |
|---|---|---|---|---|---|
| DeepSeek V4 Flash | 1189.656, 994.016, 935.669, 1082.932, 830.311 | 994.016 ms | 1190.990, 994.240, 937.296, 1083.413, 853.644 | 994.240 ms | $0.00002142, $0.00002142, $0.00001694, $0.00001778, $0.00002086; median $0.00002086 |
| GPT-5.6 Terra | 865.151, 633.137, 585.895, 597.752, 2917.122 | 633.137 ms | 927.828, 885.849, 757.099, 798.705, 3146.051 | 885.849 ms | $0.000082 each; median $0.000082 |
All five requests were sequential, streamed, concurrency one, and used the exact READY prompt. OpenAI’s fifth run was an observed 2917.122 ms TTFT and 3146.051 ms total-time outlier; it remains in the median calculation. Region was not recorded, and five samples cannot estimate worldwide or tail latency.
The matched 23-call spend was $0.0500209248 for DeepSeek and $1.683402 for OpenAI. Full applicable spend was $0.0500209248 across 23 DeepSeek calls and $1.7867516 across 32 OpenAI calls; OpenAI’s total includes nine additional applicable native-PDF and citation calls. Full-run usage was 790,200 input, 454,016 cached input, 6,014 output, and 4,897 reasoning tokens for DeepSeek; and 881,449 input, 20,948 cached input, 2,630 output, 1,179 reasoning tokens, and three web-search calls for OpenAI. Costs use provider-reported usage and frozen August 7 rates; they exclude subscriptions, taxes, retries, validation, storage, and engineering.
DeepSeek vs ChatGPT pricing
Pricing snapshot: August 7, 2026. Chat subscriptions and API usage are separate purchases. DeepSeek’s consumer chat is a different product from its metered API. ChatGPT plan prices, limits, tools, and model access vary by plan and market. Verify the official pages before approving a budget.
Consumer plan prices
| Service | Plan | Listed price | Boundary |
|---|---|---|---|
| DeepSeek Chat | Official web and mobile chat | Free access | Account, capacity, regional, and feature limits may apply; API usage is separate |
| ChatGPT | Free | $0 | Usage and tool limits apply |
| ChatGPT | Plus | $20 per month | Expanded access; availability and limits vary by mode |
| ChatGPT | Pro | $200 per month | Higher access tier; limits and safeguards still apply |
A ChatGPT subscription does not include OpenAI API credits. DeepSeek’s free consumer access does not make its developer API or self-hosting infrastructure free. Compare the exact product that will perform the work.
API token prices per 1 million tokens
| Model and effective period | Cached input | Uncached or cache-miss input | Output |
|---|---|---|---|
deepseek-v4-flash — through Aug 16, 15:59 UTC | $0.0028 | $0.14 | $0.28 |
deepseek-v4-pro — through Aug 16, 15:59 UTC | $0.003625 | $0.435 | $0.87 |
deepseek-v4-flash — from Aug 16, 16:00 UTC, off-peak | $0.007 | $0.22 | $0.66 |
deepseek-v4-flash — from Aug 16, 16:00 UTC, peak | $0.014 | $0.44 | $1.32 |
deepseek-v4-pro — from Aug 16, 16:00 UTC, off-peak | $0.022 | $0.66 | $1.98 |
deepseek-v4-pro — from Aug 16, 16:00 UTC, peak | $0.044 | $1.32 | $3.96 |
gpt-5.6-luna | $0.02 | $0.20 | $1.20 |
gpt-5.6-terra | $0.20 | $2.00 | $12.00 |
gpt-5.6-sol | $0.50 | $5.00 | $30.00 |
For the checked GPT-5.6 models, requests with more than 272K input tokens are billed at twice the input rate and 1.5 times the output rate for the full request. Cache writes are billed at 1.25 times the uncached input rate. Tool calls, Batch, Flex, Priority, regional processing, storage, taxes, and other charges can differ. DeepSeek’s scheduled cutover makes the request billing time part of the cost calculation.
Worked token-cost example
Assume 10 million uncached input tokens and 2 million output tokens, no tools, no retries, and each OpenAI request below the 272K threshold. The token-only arithmetic is:
| Model | Calculation | Estimated token charge |
|---|---|---|
deepseek-v4-flash | (10 × $0.14) + (2 × $0.28) | $1.96 |
deepseek-v4-pro | (10 × $0.435) + (2 × $0.87) | $6.09 |
gpt-5.6-luna | (10 × $0.20) + (2 × $1.20) | $4.40 |
gpt-5.6-terra | (10 × $2.00) + (2 × $12.00) | $44.00 |
gpt-5.6-sol | (10 × $5.00) + (2 × $30.00) | $110.00 |
Historical boundary: the arithmetic above intentionally preserves the August 7 rates used by the frozen comparison. It is not a post-cutover quote. This is illustrative arithmetic, not a quality comparison, workload forecast, or invoice. Use the DeepSeek pricing guide and DeepSeek API cost calculator for current DeepSeek billing scenarios.
Which is better for coding?
“Better for coding” can describe at least four jobs: answering a programming question, generating a bounded code change, operating an agent that reads and edits a repository, or serving a model inside a custom developer product. One prompt cannot rank all four.
- Chat use: compare explanation quality, correctness, language support, and how easily the user can include files or project context.
- API use: compare exact output contracts, tool calls, latency, token use, and cost per accepted change.
- Agent use: compare repository navigation, edit quality, test execution, permissions, command approval, recovery, and audit logs.
- Self-managed use: compare the deployed checkpoint, quantization, hardware, serving stack, security, and operational cost.
The original coding test is a bounded rubric result. It cannot prove that one full coding agent is universally better. Developers can review the DeepSeek API guide before designing a repository-level evaluation.
Files, images, research, and citations
ChatGPT documents a broader managed multimodal workspace, with availability depending on plan and mode. The OpenAI platform also documents image input, file input, structured outputs, web search, file search, and other hosted tools for eligible models and endpoints. These features can reduce integration work, but they may add tool, storage, or processing charges.
DeepSeek’s consumer product documents web search and file upload or text extraction. On the API, Responses now documents server-side web_search for both V4 models, while Chat Completions still has no hosted search tool. Chat uses json_object; Responses adds json_schema. Neither API fact establishes native visual-PDF input. A normalized-text test is comparable across APIs; a native-ingestion test is a capability-specific cell.
For research, citation presence is not enough. Check whether each URL loads, whether it is the requested source type, whether it supports the adjacent claim, and whether the answer adds unsupported claims. This comparison reports strict claim-level support, not a subjective impression of confidence.
Long context: capacity, retrieval, latency, and price
DeepSeek lists a 1M context window and OpenAI lists 1.05M for the checked GPT-5.6 API models. Those figures do not establish equivalent tokenization, usable capacity, output headroom, retrieval accuracy, latency, or price. The same byte stream can produce different token counts across providers.
- Name every context tier by its actual provider-reported token count.
- Place answer-key markers at declared beginning, middle, and end positions.
- Report each attempt, not only the median.
- Record truncation, cache status, output ceiling, total time, and cost.
- Apply OpenAI’s long-context multiplier when input exceeds 272K tokens.
A separate context study is more credible than a single “1M” row in a comparison table. Use the DeepSeek 1M context benchmark for deeper methodology and raw-data links.
Privacy and data control
There is no responsible one-word privacy winner. Compare consumer chat with consumer chat, business workspace with business workspace, API with API, and self-managed deployment with self-managed deployment. The product, account, region, tools, retention terms, training setting, and contract determine the relevant boundary.
- Individual ChatGPT services: OpenAI says individual content may be used to improve models unless the user opts out. Temporary Chat has separate history, memory, training, and retention behavior.
- OpenAI business and API products: OpenAI states that Business, Enterprise, and API data are not used for training by default.
- DeepSeek consumer services: DeepSeek’s privacy policy describes collecting prompts, uploaded files, photos, feedback, and chat history; using data to improve technology; jurisdiction-dependent rights; and direct processing and storage in the People’s Republic of China.
- Self-managed weights: deployment can increase infrastructure control but transfers identity, encryption, logging, backup, retention, patching, access, and incident-response responsibilities to the operator.
Do not upload credentials, private keys, confidential code, regulated records, customer data, or sensitive personal information to a consumer chatbot merely because a synthetic benchmark was safe. Review the DeepSeek privacy and security guide and obtain legal or security review when the workload requires it.
Which should you choose?
- Start with DeepSeek when published API token cost, compatible request formats, or a self-managed V4 route is central and your team can build the surrounding controls.
- Start with ChatGPT when you want a managed, integrated workspace with broad consumer tools and eligible organizational controls.
- Use the OpenAI API when native platform tools, structured outputs, multimodal inputs, or the GPT-5.6 model tiers fit the application and justify their measured cost.
- Use both when routing by workload produces better accepted-result cost or user experience than forcing a single provider.
- Run your own evaluation when the decision affects customers, confidential data, a production budget, or a high-risk workflow.
The independent DeepSeek AI guide maps the broader ecosystem. Use the DeepSeek performance evidence hub for test-specific evidence and the independent DeepSeek reliability report for service availability. Reliability and model quality are different measurements.
Reproducibility record
- Official-fact snapshot: August 7, 2026.
- API comparison lane:
deepseek-v4-flashHigh versusgpt-5.6-terraHigh. - UI comparison lanes: visible fast modes and visible reasoning modes, recorded without hidden-model inference.
- Separation rule: UI and API attempts remain separate tables, denominators, and conclusions.
- Evidence package: sanitized UI observations, isolated API aggregates, and blinded citation adjudications are retained locally with SHA-256 hashes. No public dataset download is claimed by this update.
- Protocol: Unified comparison protocol v1.0, snapshot August 7, 2026, with dated output-ceiling and long-context calibration errata.
- UI record: 16 declared cells; grader 1.1.0; input SHA-256
d806f39a875c13efb0564143c172f18524c536928ce34b185ec8e6424e5398f9; grades SHA-25600d2a4c4e0da4f41c9221dbb2bf4b9a9aa514d32e7ee8ec943ddd279bd66acb1; QA SHA-256974686d03601bf28410acafdf7bcc6ca08288dc13594eaa6b78c2be03f913847; grader SHA-25621241e0eb18222666aaea6af34c6d8b31fdcabafb18259452aeb3e1bbe2c4918; shared rubric SHA-2569e242ff4a7866cbfe3770e1e4adf9d789932361470e3eb28e49abfff83cdee29. - API record: executor 1.0.4; one isolated 55-request run; run-manifest SHA-256
8c784d6c4a1a46ad084924609addd6ffe165874fd750d64fa107da540aa1253e; aggregate-manifest SHA-256a6b24a5f8dea14e4db0f2e0e4372f02531596b76307ffc8e5b359c4f144c3995. - Citation review: two independent blinded reviewers, 100% agreement on every scored UI and API citation attempt.
- PDF fixture: SHA-256
cb3259211734feda0265ecf46cc76745e116d72782b01e5e3c136d9fa7b0a07f. - Disclosure: sanitized evidence is retained locally. This update does not claim a public raw-response archive or dataset download.
Limitations
- The original test matrix is deliberately small and supports workload-specific, not universal, conclusions.
- Small English synthetic cases will not represent every language, document, repository, user, region, or future model version.
- Consumer UI labels and API IDs are not interchangeable.
- Context capacity does not guarantee retrieval accuracy.
- Latency from one environment does not estimate worldwide performance, tail latency, or uptime.
- Token charges exclude subscriptions, tools, storage, taxes, retries, validation, engineering, and self-hosting costs.
- Official prices, models, plan limits, and features can change after the dated snapshot.
- This site is independent and is not affiliated with DeepSeek or OpenAI.
Official sources
- DeepSeek: models and pricing, updates, thinking mode, Chat Completions, Create Response, Responses API, and privacy policy.
- OpenAI API: model catalog, model comparison, GPT-5.6 Luna, GPT-5.6 Terra, and GPT-5.6 Sol.
- ChatGPT: plan comparison, file uploads, individual data controls, business data privacy, and Temporary Chat.
Update history
| Date | Update |
|---|---|
| July 24, 2026 | Rebuilt the live comparison around consumer chat, API, and self-managed deployment; refreshed model, pricing, privacy, and product distinctions. |
| August 8, 2026 | Added the completed 16-cell web-app matrix and isolated 55-request API run, with two-reviewer citation adjudication, six verified figures, exact denominators, N/A cells, the retained timeout, costs, latency observations, and reproducibility hashes. Tested ChatGPT Instant and High, DeepSeek Instant and Expert, deepseek-v4-flash High, and gpt-5.6-terra High. |
| August 14, 2026 | Updated only the current DeepSeek documentation layer for V4-Pro-0813 GA, Responses support on both V4 models, endpoint-specific search and structured output, reasoning-effort mapping, and the August 16 price cutover. Historical tests and costs remain unchanged. |
Frequently asked questions
Is DeepSeek better than ChatGPT?
Not universally. DeepSeek is attractive for low published API prices and self-managed deployment options. ChatGPT provides the broader managed consumer workspace. Quality must be tested on the exact task, surface, plan, model, and settings.
Is DeepSeek cheaper than ChatGPT?
DeepSeek’s current V4 API token rates are lower than the checked GPT-5.6 rates, but ChatGPT subscriptions and API usage are different products. Total cost also includes tools, validation, retries, engineering, and any self-hosting infrastructure.
Are DeepSeek and ChatGPT free?
Both offer consumer access with limits that can change. Their developer APIs are metered separately. A free chat plan is not API credit and should not be described as unlimited or permanent.
Which is better for coding, DeepSeek or ChatGPT?
It depends on whether you need chat help, a model endpoint, a full coding agent, or a self-managed model. Compare correctness, repository work, tests, tool permissions, latency, and accepted-change cost rather than one code prompt.
Which API is cheaper, DeepSeek or OpenAI?
DeepSeek V4 has lower listed token rates in the August 7, 2026 snapshot. That does not prove lower cost per accepted production result. Measure usage, tools, retries, validation, latency, and downstream labor on your workload.
Which has the larger context window?
The checked DeepSeek V4 API models list 1M tokens, while the checked GPT-5.6 API models list 1.05M. These limits do not guarantee equal tokenization, retrieval accuracy, latency, or cost.
Are ChatGPT results the same as OpenAI API results?
No. ChatGPT plan features, visible modes, routing, limits, and fallbacks differ from named API requests. This page keeps UI and API evidence separate.
Which is more private, DeepSeek or ChatGPT?
There is no safe answer without naming the product, plan, deployment, region, tools, retention terms, and data. Compare current written policies and contracts for the exact route, minimize data, and obtain appropriate security or legal review.