DeepSeek vs Llama is not a one-model contest. DeepSeek V4 is a text-focused model family with a first-party hosted API and MIT-licensed weights. Llama 4 is Meta’s open-weight family for text and image input, normally accessed through downloaded weights or a separate inference provider.
Tested and updated July 28, 2026: we ran DeepSeek V4 Flash through DeepSeek’s first-party API and Llama 4 Maverick FP8 through DeepInfra, routed exclusively by OpenRouter. Both models completed the same English-only structured-decision, coding, and 120K-token retrieval fixtures. Maverick also completed a synthetic chart-reading test because the tested DeepSeek V4 endpoint does not accept native image input.
The result is not a universal winner. In this bounded run, both endpoints answered all three decision tasks correctly after one disclosed Llama retest, but only DeepSeek returned raw JSON on all three. DeepSeek passed all six coding tests; Llama passed three. Both recovered all three facts from the 120K-token document. Llama correctly extracted all five requested chart facts, while DeepSeek was marked N/A rather than failed.
This article compares model families and developer access, not the Meta AI consumer assistant. For the chat-product comparison, see DeepSeek vs Meta AI.
DeepSeek vs Llama: Quick Answer
| If you need… | Start by testing… | Evidence behind the recommendation |
|---|---|---|
| A direct, low-cost hosted text API | DeepSeek V4 Flash | DeepSeek publishes a first-party endpoint and price; our tested Flash route was less expensive than the named Llama route on every matched fixture |
| Strict raw JSON for a text workflow | DeepSeek V4 Flash | It returned raw JSON on 3/3 structured tasks; Maverick returned prose or Markdown fences on 3/3 |
| Native image and text input | Llama 4 Maverick or Scout | Meta officially supports image input; Maverick recovered 5/5 facts from our synthetic chart |
| Executable code for a specific repository | Run your own test | DeepSeek won our single six-test fixture, but one small task cannot establish a general coding winner |
| More than 1M advertised context | Llama 4 Scout | Meta lists 10M for Scout, with important training and hardware caveats |
| A standard permissive model license | DeepSeek V4 | DeepSeek V4 code and weights are released under MIT; Llama uses a custom community license |
| Self-hosting and infrastructure control | Depends on the model, license, and hardware | Both families offer weights, but their memory, serving, and license requirements differ substantially |
Bottom line: DeepSeek V4 Flash was the more reliable structured-output and coding endpoint in our small text suite, and it had the lower measured task cost on the two routes we used. Llama 4 Maverick added a capability the tested DeepSeek endpoint did not offer: native image understanding. Those are specific findings, not proof that one family is better for every prompt, provider, or deployment.
Which DeepSeek and Llama Models Are Being Compared?
A reproducible comparison needs exact model IDs and access routes. “DeepSeek” and “Llama” are product families, not test configurations. The live head-to-head in this article used the following endpoints:
| Tested endpoint | Access route | Mode and format | Provider limit recorded in the test |
|---|---|---|---|
deepseek-v4-flash | DeepSeek first-party API | Thinking disabled; text input | 1M official context; up to 384K output |
meta-llama/llama-4-maverick | DeepInfra FP8 through OpenRouter, with routing restricted to DeepInfra | Text and image input; requested seed 42 | 1,048,576 context; 16,384 maximum output on the tested route |
We did not live-test DeepSeek V4 Pro, Llama 4 Scout, a local checkpoint, or another Llama hosting provider. Statements about those configurations below come from first-party documentation and are labeled as specifications, not benchmark results.
Current DeepSeek V4 lineup
A live request to DeepSeek’s model-list endpoint returned deepseek-v4-flash and deepseek-v4-pro. Flash has 284B total parameters and 13B active parameters. Pro has 1.6T total and 49B active. Both officially list a 1M context window and maximum output up to 384K.
DeepSeek’s documentation supports thinking and non-thinking operation, JSON output, tool calls, and chat-prefix completion. Fill-in-the-middle completion is documented for non-thinking mode. See the site’s DeepSeek V4 architecture guide, thinking-mode guide, and API guide for implementation details.
The old deepseek-chat and deepseek-reasoner retirement deadline passed on July 24, 2026. Both aliases still returned HTTP 200 in our July 28 account test and resolved to deepseek-v4-flash, but this is dated compatibility behavior, not a promise. New code should use the explicit V4 model IDs.
Current Llama 4 lineup
Meta’s released Llama 4 family consists of Scout and Maverick. Scout has 109B total parameters, 17B active parameters, and 16 experts. Maverick has 400B total, 17B active, and 128 experts. Both are mixture-of-experts models with native early-fusion multimodality: multilingual text and images go in, and multilingual text or code comes out.
Meta lists a 10M context window for Scout and 1M for Maverick. The Scout number needs context: Meta says Scout was pre-trained and post-trained at 256K, then length-generalized and tested up to 10M. It should not be interpreted as proof of uniform quality at every length. Meta also says image understanding was tested with up to five input images. Llama 4 does not generate images, and the official supported-input list does not include video.
Behemoth was previewed but has not been released as a current downloadable Llama 4 checkpoint, so it is excluded from this comparison.
| Model | Total / active parameters | Official context | Native input | Access and license |
|---|---|---|---|---|
| DeepSeek V4 Flash | 284B / 13B | 1M | Text in the current core API | First-party API; MIT-licensed weights |
| DeepSeek V4 Pro | 1.6T / 49B | 1M | Text in the current core API | First-party API; MIT-licensed weights |
| Llama 4 Scout | 109B / 17B; 16 experts | 10M listed, with 256K training caveat | Text and images | Gated weights; Llama 4 Community License |
| Llama 4 Maverick | 400B / 17B; 128 experts | 1M | Text and images | Gated weights; Llama 4 Community License |
How We Tested DeepSeek V4 Flash vs Llama 4 Maverick
The test used synthetic English-only fixtures created for this article. No personal, customer, account, or confidential business data was submitted. Credentials and response identifiers were excluded from the saved evidence.
- Test time: July 28, 2026 at 15:09 UTC.
- DeepSeek route: first-party API,
deepseek-v4-flash, thinking disabled. - Llama route:
meta-llama/llama-4-maverick, DeepInfra FP8 through OpenRouter, with no fallback provider. - Sampling: temperature 0 for both; seed 42 requested on the Llama route.
- Retries: no transport retries and no quality retries. One Llama capacity task was rerun with a larger disclosed output allowance after its first answer ended before the final result.
- Scoring: exact expected answers, raw-JSON compliance, executable unit tests, exact long-context codes, and exact chart facts.
- Cost: DeepSeek estimates use its July 28 cache-miss input and output rates. Llama cost is the amount reported for the named routed endpoint.
Elapsed time and price are properties of the named routes under the conditions observed. They are not pure model-speed or universal model-cost scores. A different Llama provider, region, quantization, load level, or runtime can change both.
Live Benchmark Results
| Test | DeepSeek V4 Flash | Llama 4 Maverick | Bounded finding |
|---|---|---|---|
| Three structured decisions | 3/3 correct; 3/3 raw JSON only | 3/3 correct after one disclosed retest; 0/3 raw JSON only | DeepSeek followed the output-only constraint more reliably in this small suite |
| Python coding task | 6/6 unit tests | 3/6 unit tests | DeepSeek implemented both overlap and adjacency requirements; Llama missed adjacency |
| About 120K prompt tokens | 3/3 planted codes | 3/3 planted codes | Both succeeded at the tested length |
| Synthetic chart | N/A: tested endpoint has no native image input | 5/5 requested facts | Llama completed the visual task; this was a capability test, not a fabricated head-to-head score |
Structured decisions and JSON adherence
The structured suite contained three deterministic business-style problems: a project schedule constrained by delivery and review time, a capacity-constrained order-selection problem, and a pricing break-even calculation. Each prompt specified the expected JSON fields and prohibited extra prose.
| Task | DeepSeek V4 Flash | Llama 4 Maverick |
|---|---|---|
| Project Falcon schedule | Correct; raw JSON; 2.126 s; estimated $0.00004984 | Correct; explanatory prose plus fenced JSON; 10.482 s; reported $0.000307 |
| Capacity-constrained orders | Correct; raw JSON; 1.120 s; estimated $0.00002702 | Initial 500-token answer ended before the result; correct at 1,200 tokens, with long derivation before JSON; 33.733 s on retest; reported $0.0008688 |
| Pricing break-even | Correct; raw JSON; 1.129 s; estimated $0.00001820 | Correct; JSON inside Markdown fences; 1.346 s; reported $0.0000406 |
Across these three tasks, DeepSeek’s estimated cost was $0.00009506. The Llama route reported $0.0012164 after the disclosed capacity retest. This does not mean every Llama deployment costs about 13 times more. It means this particular DeepInfra route produced a longer response and charged more than the tested first-party DeepSeek route on these prompts.
The practical lesson is larger than the score: validate JSON with a schema parser instead of trusting a model’s formatting instruction. If an endpoint repeatedly adds fences or prose, use the provider’s structured-output feature where available and test malformed, truncated, and refusal cases. The site’s DeepSeek JSON output guide covers production validation patterns.
Coding test with executable unit tests
Both endpoints received the same Python task: implement merge_intervals, merge overlapping and touching integer intervals, and do not mutate the input. The six tests covered overlap, adjacency, unsorted data, duplicates, negative values, and input preservation.
DeepSeek V4 Flash passed 6/6 tests. Llama 4 Maverick passed 3/6. The Llama implementation merged ordinary overlaps but did not merge adjacent intervals such as [1,3] and [4,7]. DeepSeek’s run took 1.409 seconds and cost an estimated $0.00004466; the Llama route took 2.949 seconds and reported $0.0001038.
This is evidence for one requirement-sensitive function, not a general coding leaderboard. A production evaluation should use tasks from the target repository, execute tests in isolation, score unwanted file changes, and include repair attempts in cost. For a reusable scoring structure, see the DeepSeek evaluation framework.
120K-token long-context retrieval
We generated 7,500 synthetic records and planted three audited codes near the beginning, midpoint, and end. The expected values were ALPHA-731, MID-442, and OMEGA-908. The providers reported approximately 120,000 prompt tokens for each request.
Both endpoints returned all three codes correctly. DeepSeek completed the observed request in 3.254 seconds at an estimated $0.01681848. The Llama route completed it in 6.709 seconds at a reported $0.024042. The result supports one narrow conclusion: both tested routes successfully retrieved three clearly defined facts at about 120K tokens. It does not validate either model’s advertised maximum window.
For a real document system, repeat the test with contradictory evidence, similar distractors, multiple answer spans, and facts placed at varying depths. Measure citation accuracy and answer completeness, not only whether one planted code can be copied.
Vision and chart understanding
The visual fixture was a synthetic June 2026 support-ticket bar chart. The prompt asked for four regional values and the highest region. The expected facts were North 84, South 61, East 73, West 92, with West highest.
Llama 4 Maverick returned all 5/5 facts correctly in 1.411 seconds. The route reported 2,380 prompt tokens, 32 completion tokens, and $0.0005016. DeepSeek V4 was marked N/A: the tested core Chat Completions endpoint accepts text content rather than native image input. Marking an unsupported modality as a failed answer would distort the comparison.
Architecture: Two Different MoE Strategies
All four models in this comparison use mixture-of-experts architecture. Only a subset of parameters is active for a token, but active parameters are a compute description, not the model’s storage requirement. The full weight set still needs to be resident, streamed, or offloaded by the serving system.
DeepSeek V4
DeepSeek’s V4 technical materials describe a hybrid attention design combining Compressed Sparse Attention and Heavily Compressed Attention, mHC residual connections, the Muon optimizer, and more than 32T pretraining tokens. The instruct releases use FP4 for mixture-of-experts components and FP8 for most other parameters, while the base checkpoints use an FP8 mixed format.
The hosted API exposes non-thinking and thinking behavior rather than requiring users to serve the checkpoints. For many teams, that direct route matters more than architecture alone: it removes the need to choose an inference engine, quantization, GPU topology, and autoscaling policy.
Llama 4
Llama 4 uses native early fusion, allowing text and image tokens to participate in the same model. In Maverick, each token uses a shared expert plus one routed expert, while the serving system still stores the complete model. Scout activates the same 17B parameter scale with fewer total parameters and experts, which changes its deployment profile.
Architecture can explain capability or cost hypotheses, but it does not settle output quality. Prompt template, quantization, provider implementation, reasoning budget, runtime, and tool scaffolding can produce meaningful differences between two deployments of the same checkpoint.
Context Windows: 1M vs 10M Without the Marketing Shortcut
DeepSeek V4 Flash, DeepSeek V4 Pro, and Llama 4 Maverick officially list 1M context. Llama 4 Scout lists 10M. DeepSeek also lists up to 384K output, while hosted Llama output limits depend on the provider; our Maverick route exposed 16,384 maximum output tokens.
Three distinctions matter:
- Model specification versus provider limit: a hosted service may expose less context or output than the underlying checkpoint supports.
- Acceptance versus retrieval: accepting a long prompt does not guarantee reliable recovery of buried or contradictory evidence.
- Maximum context versus hardware feasibility: KV cache, prefill time, memory, and concurrency grow with sequence length. Scout fitting on a GPU under one quantized configuration does not prove that serving 10M tokens on that GPU is practical.
Our matched test stopped at about 120K, where both endpoints succeeded. It would be misleading to call Scout the retrieval winner from its 10M specification or DeepSeek the long-context winner from a single 120K timing. Test the lengths, evidence positions, and concurrency your workload will actually use.
Multimodal Input, JSON, and Tool Calling
Llama 4 has the documented modality advantage. Scout and Maverick accept multilingual text and images and produce text or code. This makes them candidates for charts, screenshots, photographed documents, and product images. The model card says image understanding was evaluated with up to five input images.
The current DeepSeek V4 Chat Completions schema requires user content as a text string, and the official V4 checkpoint pages classify the models as text generation. We therefore treat the tested V4 core endpoint as text-only. This does not describe every third-party wrapper or separate DeepSeek vision product.
DeepSeek documents JSON output and tool calls. Llama deployments can also support structured output and tools, but the exact behavior depends on the instruction checkpoint, prompt template, inference server, and provider API. Our live suite tested formatting instructions, not native schema enforcement or function calling. Use the site’s DeepSeek tool-calling guide when building a production test matrix.
DeepSeek vs Llama API Pricing
DeepSeek publishes a direct first-party API price. As checked on July 28, 2026, the official prices per 1M tokens were:
| Model | Cache-hit input | Cache-miss input | Output |
|---|---|---|---|
| DeepSeek V4 Flash | $0.0028 | $0.14 | $0.28 |
| DeepSeek V4 Pro | $0.003625 | $0.435 | $0.87 |
Current DeepSeek API pricing (verified August 16, 2026): DeepSeek uses peak rates during 01:00–04:00 and 06:00–10:00 UTC and off-peak rates at all other times. Flash off-peak / peak rates are $0.007 / $0.22 / $0.66 and $0.014 / $0.44 / $1.32; Pro off-peak / peak rates are $0.022 / $0.66 / $1.98 and $0.044 / $1.32 / $3.96, in cache-hit input / cache-miss input / output order. Use the current pricing page and time-aware cost calculator for new budgets. Any dated prices, test costs, or arithmetic elsewhere on this page remain historical and have not been recomputed.
DeepSeek states that prices can change. Check the live DeepSeek pricing page before budgeting.
Llama 4 has no universal first-party hosted inference price. Meta provides gated weights, while third-party providers set their own context limits, quantization, regions, and rates. During our test, the DeepInfra route through OpenRouter listed $0.20 per 1M prompt tokens and $0.80 per 1M output tokens. That is a route price, not “the price of Llama 4.”
Self-hosting replaces per-token pricing with infrastructure economics: GPU rental or depreciation, storage, networking, idle capacity, engineering, monitoring, failover, and utilization. Compare cost per successful task, including retries and repair, rather than only advertised token rates.
Access and Deployment Requirements
Getting Llama 4 weights
The official Llama 4 repositories on Hugging Face are gated. A requester must accept Meta’s license and provide legal identity and organization details. Meta’s direct download flow also requires license acceptance and approval. After approval, Meta emails a signed download URL for the llama-models command-line tool; the URL expires after 24 hours and is subject to download limits.
There is not one current, universal Meta-hosted Llama 4 API with a standard first-party price. Meta’s present consumer and model-API products should not be assumed to expose Llama 4. If you use a cloud or inference provider, record its exact model ID, region, quantization, context cap, output cap, data terms, and deprecation policy.
Llama 4 hardware caveats
Meta’s reference materials state that full BF16 Llama 4 inference needs at least four GPUs. For Scout, FP8 needs two 80GB GPUs, while on-the-fly INT4 can fit one 80GB H100. Maverick at FP8 fits a single H100 DGX host, which is a multi-GPU system, not one H100 GPU.
Those statements describe reference configurations, not guaranteed production capacity. Long prompts, image tokens, KV cache, batching, concurrency, runtime overhead, and safety layers all add memory and compute demand. Benchmark the complete service, including cold start and recovery, before making a hardware purchase.
Running DeepSeek V4 weights
DeepSeek publishes V4 weights and an inference reference. Its example uses model parallelism of eight and supports multi-node execution. DeepSeek does not publish one authoritative minimum-VRAM figure that covers every V4 model, precision, context length, and runtime, so a precise universal number would be misleading.
V4 Flash is smaller than V4 Pro, but neither is a routine laptop deployment. Teams evaluating local use should measure weight storage, loaded memory, quantization quality, prefill, decode throughput, KV cache, concurrency, and operational recovery. The local DeepSeek guide covers the practical setup path without duplicating it here.
Licensing and Commercial Use
DeepSeek V4’s repository and model weights use the MIT license. Llama 4 uses the custom Llama 4 Community License and Acceptable Use Policy. Both families are available as open weights, but the licenses are not equivalent.
| Question | DeepSeek V4 | Llama 4 |
|---|---|---|
| Model license | MIT | Llama 4 Community License |
| Standard permissive license | Yes | No; custom Meta terms apply |
| Attribution and redistribution | Retain the MIT notice | Provide the license and notice and display “Built with Llama” when required |
| Derived distributed AI model naming | No Llama-specific naming rule | A qualifying model trained or improved using Llama output must begin with “Llama” |
| Very large consumer platform condition | No comparable MIT threshold | Organizations above the stated 700M monthly-active-user threshold require a separate Meta license |
| Acceptable-use policy | No separate V4 model use policy in the MIT license | Meta Acceptable Use Policy applies |
Use “open-weight” rather than treating Llama 4 as equivalent to an OSI-approved open-source software release. Read the current license and policy directly before redistribution, model derivatives, or large commercial deployment. This section is a factual comparison, not legal advice.
Which Model Should You Choose?
| Workload | Best first test | What to validate |
|---|---|---|
| Bulk extraction, classification, and JSON | DeepSeek V4 Flash | Schema validity, retries, cache hit rate, and cost per accepted record |
| Difficult text reasoning or coding | DeepSeek V4 Pro and Llama 4 Maverick | Repository tests, reasoning budget, latency, and repair cost; our live suite did not test Pro |
| Charts, screenshots, and image-grounded documents | Llama 4 Maverick or Scout | Exact visual facts, OCR errors, multi-image limits, and provider image handling |
| About 100K to 1M text context | Test both families on the same provider constraints | Retrieval by position, contradictions, citations, prefill time, and cost |
| More than 1M advertised context | Llama 4 Scout | Actual provider limit, 10M quality, memory, prefill, and concurrency |
| Direct managed API with one published price | DeepSeek | Regional availability, data terms, rate limits, and fallback behavior |
| Self-hosted weights with the simplest license | DeepSeek, if the infrastructure is feasible | Model size, quantization, serving runtime, and total cost |
| Self-hosting through a mature Llama ecosystem | Llama 4 | Community-license obligations, exact hardware, runtime support, and quality after quantization |
A router can be sensible when text, hard reasoning, and image tasks have different requirements. Route from an explicit capability matrix, retain fallbacks, and monitor quality drift. If alternatives are in scope, compare the same fixtures in DeepSeek vs Qwen or DeepSeek vs Mistral instead of relying on vendor benchmark tables.
How to Reproduce the Comparison
- Pin the exact model ID, provider, route, region, quantization, API version, prompt format, and test date.
- Use synthetic or approved English fixtures with pre-registered expected answers and scoring rules.
- Align temperature, top-p, maximum output, system prompt, and tool schemas where the APIs allow it. Disclose controls that cannot be matched.
- For structured tasks, parse every response against the schema. Count fences, prose, truncation, and repair calls.
- For coding, compile or execute every answer in an isolated environment and publish the unit tests.
- For long context, plant evidence at multiple positions and add similar distractors and contradictions. Report the provider’s token count.
- Run image tasks separately and mark unsupported inputs N/A. Do not convert a modality difference into a quality failure.
- Alternate request order, use repeated runs for variable tasks, and report p50 and p95 rather than promoting one elapsed time.
- Record input/output tokens, reasoning tokens where returned, finish reason, retries, status, and cost per correct result.
- Publish sanitized prompts, fixtures, scoring code, and outputs without keys, account details, request identifiers, or private data.
Limitations
- The live benchmark is intentionally small and is not statistically significant.
- Only DeepSeek V4 Flash and Llama 4 Maverick were tested. Results do not score V4 Pro or Scout.
- Llama price and latency belong to DeepInfra FP8 through OpenRouter. Another Llama route can behave differently.
- DeepSeek cost uses the official cache-miss rates checked on July 28, 2026. A cache hit would change the input cost.
- The Llama capacity task required a disclosed 1,200-token retest after the 500-token run ended before the final answer.
- The context fixture used about 120K prompt tokens, not the 1M or 10M advertised limits.
- No local checkpoint was loaded, so this article does not measure self-hosted memory, throughput, or latency.
- Vendor-reported architecture and benchmark claims are treated as specifications, not independent proof of a winner.
FAQ: DeepSeek vs Llama
Is DeepSeek better than Llama 4?
There is no universal winner. DeepSeek V4 Flash was stronger on raw-JSON adherence and the one executable coding task in our bounded test. Llama 4 Maverick completed the native image task that the tested DeepSeek endpoint could not accept. Choose from matched workload evidence, deployment needs, and license terms.
Which is better for coding, DeepSeek or Llama?
DeepSeek passed 6/6 unit tests and Llama passed 3/6 on our merge-intervals fixture. That supports DeepSeek for this task only. Test both on the target repository, with compilation, unit tests, repair attempts, and cost per accepted patch.
Which has the larger context window?
Llama 4 Scout lists 10M context. Llama 4 Maverick and both DeepSeek V4 models list 1M. Scout was trained at 256K and length-generalized and tested to 10M, so the maximum should not be treated as guaranteed retrieval quality. Our live comparison tested only about 120K.
Does DeepSeek V4 support images?
The current official V4 Chat Completions schema and the endpoint we tested are text-focused and do not accept native image input. Llama 4 Scout and Maverick officially accept text and images. A third-party vision wrapper should not be confused with native V4 input.
Can Llama 4 Scout run on one GPU?
Meta says Scout can fit one 80GB H100 with on-the-fly INT4 quantization. Scout FP8 needs two 80GB GPUs in Meta’s reference guidance. One-GPU weight fit does not guarantee that very long context or production concurrency will fit.
Can Llama 4 Maverick run on one H100?
No official source supports describing Maverick FP8 as a one-H100-GPU model. Meta says it fits one H100 DGX host, which contains multiple H100 GPUs. State the complete system when comparing hardware requirements.
Is Llama 4 open source?
Open-weight is the more precise description. Meta distributes Llama 4 weights under the custom Llama 4 Community License, which contains attribution, redistribution, acceptable-use, naming, and large-platform conditions. DeepSeek V4 uses MIT.
Which API is cheaper?
DeepSeek V4 Flash was cheaper than the tested DeepInfra Llama 4 Maverick route on every matched task in this article. Llama has no single universal hosted price, so compare the exact provider, context, quantization, and output behavior. Self-hosting requires a separate total-cost calculation.
Are Meta AI and Llama 4 the same product?
No. Llama 4 is an open-weight model family. Meta AI is a consumer assistant and should not be used as a proxy benchmark for an identified Llama 4 checkpoint or provider route. Read the separate DeepSeek vs Meta AI comparison for that product-level question.
Update Log
- July 28, 2026: added original matched API tests for structured decisions, executable Python, about 120K tokens, and a synthetic chart; documented exact routes, settings, token usage, costs, and limitations.
- July 28, 2026: corrected the passed legacy-alias deadline, clarified Scout’s 256K training and 10M length-generalization statement, distinguished one H100 GPU from one H100 DGX host, and separated Llama 4 from Meta AI.
- July 28, 2026: refreshed DeepSeek V4 pricing, model IDs, architecture, modality, licensing, and deployment notes from first-party sources.
Official Sources
- DeepSeek V4 Preview Release
- DeepSeek API Updates
- DeepSeek Models and Pricing
- DeepSeek Chat Completions API Schema
- DeepSeek V4 Flash Model Card
- DeepSeek V4 Pro Model Card
- DeepSeek V4 Reference Inference Guide
- DeepSeek V4 Technical Report
- Meta Llama 4 Announcement
- Official Llama 4 Model Card
- Official Llama 4 Scout Repository
- Official Llama 4 Maverick Repository
- Llama 4 Community License
- Llama Acceptable Use Policy
- Meta Llama Access and Downloads
Editorial disclosure: this comparison is independent and based on first-party documentation plus the original tests described above. Provider credentials, account details, response identifiers, and billing information were not retained. Product specifications and prices can change; check the linked official sources before deployment.