Mistral Large 4 vs DeepSeek V4 Pro: Complete Comparison and Report on Pricing, Peak-Hour Rates, Benchmarks, and Open-Weight Licenses

Mistral Large 4 and DeepSeek V4 Pro are two of the largest open-weight Mixture-of-Experts models a developer can call today from a first-party API with a 1-million-token context window. Mistral opened Large 4 as a public preview on October 6, 2026, and released the open weights of the improved model on October 29, 2026, under the Mistral Ecosystem Open-Weight License. DeepSeek published V4 Pro as a preview on April 24, 2026 and moved it to general availability on August 13, 2026 as DeepSeek-V4-Pro-0813, the version served today under the deepseek-v4-pro API name, with weights under the MIT license.

On the measurements that can be compared directly, Mistral Large 4 comes out ahead. Artificial Analysis scores Mistral Large 4 (high) at 26 on its Intelligence Index, against 23 for DeepSeek V4 Pro 0813 (max), and Mistral Large 4 at medium effort already matches DeepSeek's maximum-effort score of 23 at $1.41 per index task instead of $1.77. On price per token the two are almost level during DeepSeek's off-peak hours: Mistral charges $0.70 input and $2.10 output per million tokens, DeepSeek charges $0.66 and $1.98 off-peak, and DeepSeek doubles those rates to $1.32 and $3.96 during its weekday peak windows. DeepSeek V4 Pro keeps two practical advantages, a published output ceiling of up to 384K tokens and the plain MIT license with no restriction on model-as-a-service resale, while Mistral Large 4 accepts images and DeepSeek V4 Pro does not.

The better choice therefore depends on the workload. Teams that need image and document input, a European deployment option or the higher aggregate score should pick Mistral Large 4; teams that run large batch jobs they can schedule into DeepSeek's off-peak hours, or that plan to resell hosted inference of the weights, have a concrete reason to pick DeepSeek V4 Pro. All prices in this comparison were checked on the official Mistral and DeepSeek pricing pages on November 3, 2026.

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MISTRAL LARGE 4 AND DEEPSEEK V4 PRO: RELEASE TIMELINE AND MODEL IDENTITY.

The two models arrived six months apart and reached their current versions by different routes. DeepSeek V4 Pro went through a long public life before its general-availability release: the April preview introduced the 1.6-trillion-parameter model and the 1M-token context that DeepSeek made the default across its services, and the August 13 release, which DeepSeek describes as bringing "major Agent upgrades with strong production gains", added adjustable reasoning effort and native support for the OpenAI Responses API. When DeepSeek launched V4.1 Flash on September 10, its changelog stated that V4 Pro API service would continue after September 14 with billing unchanged, and V4 Pro remains on the current Models and Pricing page next to deepseek-flash.

Mistral Large 4 moved faster. The preview opened in Mistral Studio on October 6 with a two-week 50% launch discount, and the open-weight release on October 29 brought a revised model that Mistral says improved on the preview in several areas, including Terminal-Bench 4.0 (from 28% to 41%) and FinWorkBench (from 67.4% to 73.3%). Mistral's AI-governance page lists the October 29 version as active and the October 6 preview as retired. The arrival of Large 4 also closes the previous generation: Mistral's documentation marks Mistral Large 3 (mistral-large-2512) as deprecated from October 31, 2026 and tells developers to use Large 4 for new integrations.

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Milestone	Mistral Large 4	DeepSeek V4 Pro
First public release	October 6, 2026 (public preview)	April 24, 2026 (preview)
Current version	October 29, 2026 open-weight release	DeepSeek-V4-Pro-0813, August 13, 2026
API model name	mistral-large-4 (also mistral-large-4-0)	deepseek-v4-pro
Predecessor status	Mistral Large 3 deprecated October 31, 2026	V4 Pro preview replaced by the 0813 GA version
Weights on Hugging Face	mistralai/Mistral-Large-4-1T-A52B and an NVFP4 variant	deepseek-ai/DeepSeek-V4-Pro and DeepSeek-V4-Pro-0813
License	Mistral Ecosystem Open-Weight License (modified MIT)	MIT

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The Mistral documentation page for the model still shows the status "Public Preview" and the version tag 26.10, even though the open weights and the governance listing describe the October 29 model as the active release. Developers who pin model names should use mistral-large-4-0 and watch the Mistral changelog, which at the time of writing had no entry for the October 29 version.

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MISTRAL LARGE 4 VS DEEPSEEK V4 PRO API PRICING AND DEEPSEEK PEAK-HOUR RATES.

Mistral uses a single price list for Large 4. DeepSeek has used time-of-day pricing since 16:00 UTC on August 16, 2026: off-peak rates are half of peak rates, and the peak windows run from 01:00 to 04:00 and from 06:00 to 10:00 UTC, Monday through Friday, excluding Chinese public holidays. Every other hour, including weekends, is billed at the off-peak rate. Those windows cover 35 of the 168 hours in a week, about 21%, and they fall in the European morning, so a European team calling DeepSeek during its working day pays the peak price for part of it.

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Price per 1M tokens (USD)	Mistral Large 4	DeepSeek V4 Pro off-peak	DeepSeek V4 Pro peak
Input (cache miss)	$0.70	$0.66	$1.32
Cached input (cache hit)	$0.07	$0.022	$0.044
Output	$2.10	$1.98	$3.96
Cache discount on input	90%	about 97%	about 97%
Web search / fetch tools	Not priced on the model page	$0.75 per 1,000 requests each	$0.75 per 1,000 requests each

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At off-peak rates DeepSeek is about 6% cheaper than Mistral on uncached input and output, and its cached input costs less than a third of Mistral's. At peak rates DeepSeek costs 1.9 times Mistral on input and output. Mistral's general pricing page states that batch processing is billed at 50% off, while the model page lists only the standard rates; DeepSeek publishes no separate batch price, and its off-peak window works as its discount mechanism. DeepSeek also began per-request billing for server-side tool calls made through DeepSeek Harness, Claude Code or Codex at 00:00 Beijing time on November 1, 2026, which adds a cost for agents that rely on DeepSeek's built-in search and fetch tools.

For background on how DeepSeek bills accounts, granted balances and peak rates, see our guide to DeepSeek API pricing and developer access.

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CONTEXT WINDOW, OUTPUT LIMITS AND REASONING EFFORT IN MISTRAL LARGE 4 AND DEEPSEEK V4 PRO.

Both models accept 1 million tokens of context through their first-party APIs, and both expose three reasoning effort levels, although the levels are named and positioned differently. Mistral's model card lists low, medium and high, with high as the default and recommended for complex prompts and agentic work; the low level replaces the earlier "None" mode, so Large 4 always reasons to some degree. DeepSeek added effort control in the August 13 release with low, high and max, keeps thinking on by default, and still offers a non-thinking mode, which is also the only mode that supports fill-in-the-middle completion.

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Specification	Mistral Large 4	DeepSeek V4 Pro
Context window	1M tokens	1M tokens
Maximum output	Not stated in the documentation	Up to 384K tokens
Reasoning effort levels	low, medium, high (default high)	low, high, max; non-thinking mode available
Input modalities	Text and images	Text only (vision not supported)
Function calling and structured output	Yes	Yes (tool calls, JSON output)
API formats	Mistral chat completions, Agents and Conversations, batching	OpenAI format, Anthropic format, OpenAI Responses API
Concurrency limit	Per-model limits shown in the Mistral admin panel	500 concurrent requests

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The output ceiling matters for long reasoning runs. Mistral's model card warns that max_completion_tokens counts reasoning tokens as well as visible output, and the documentation gives no maximum, so developers have to test the practical limit. DeepSeek publishes 384K tokens and recommends a context of at least 384K when running Think Max locally. The image gap is the larger functional difference: Large 4 includes a 1.6-billion-parameter vision encoder and handles documents, charts and natural images, while the DeepSeek pricing page marks vision as unsupported for V4 Pro and supported only for deepseek-flash.

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ARCHITECTURE AND SELF-HOSTING FOOTPRINT OF THE TWO OPEN-WEIGHT MODELS.

The two designs share more than their size suggests. Mistral Large 4 uses Multi-head Latent Attention with a DeepSeek Sparse Attention indexer that selects up to 2,048 key positions per query, an approach DeepSeek introduced in earlier releases. DeepSeek V4 Pro moved on to a hybrid attention that combines Compressed Sparse Attention and Heavily Compressed Attention, and DeepSeek reports that at 1M tokens V4 Pro needs 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek V3.2.

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Architecture	Mistral Large 4	DeepSeek V4 Pro
Total parameters	1.05T (documentation)	1.6T (model card)
Active parameters per token	49B, or 52B with embeddings and output layers	49B
Expert routing	4 of 128 routed experts plus 1 shared expert	Mixture-of-Experts (routing details in the technical report)
Layers	72 decoder layers, first layer dense	Not stated on the model card
Attention	MLA with DeepSeek Sparse Attention indexer	Hybrid CSA and HCA
Released precision	FP8 and NVFP4 checkpoints	FP4 experts with FP8 for most other weights
Pretraining data	Not disclosed	More than 32T tokens

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Running either model locally takes a multi-GPU server. Hugging Face's launch post for Large 4 gives an FP8 recipe on eight NVIDIA H200 GPUs, a Dell example on a four-GPU B300 node with the NVFP4 checkpoint, and reports that 2-bit community quantizations run on a single Apple Silicon Ultra machine with 512 GB of unified memory at about 15 tokens per second. Mistral itself cautions on the model card that local serving "may be subpar" and recommends its API for optimal performance. DeepSeek's model card recommends a temperature and top_p of 1.0 for local deployment and ships the weights in mixed FP4 and FP8, and Hugging Face lists the 0813 repository at 1.7T parameters.

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OPEN-WEIGHT LICENSES: MISTRAL ECOSYSTEM LICENSE VERSUS MIT.

The license is the clearest legal difference between the two models. DeepSeek publishes V4 Pro under the MIT license, which permits use, modification, redistribution and commercial hosting without further conditions beyond the copyright notice. Mistral describes its Ecosystem Open-Weight License as a modified MIT license: use, modification, fine-tuning and commercial use are allowed, but a business that operates a Model-as-a-Service offering must obtain a separate commercial license from Mistral before using the model commercially. Mistral's announcement names no revenue threshold for that condition.

For a company that runs Large 4 inside its own products or on its own servers, the two licenses work in a similar way. For a cloud provider, an inference startup or a reseller that wants to sell API access to the weights, DeepSeek V4 Pro can be hosted immediately and Mistral Large 4 requires a negotiation. That difference already shows in availability: Artificial Analysis counts 9 API providers serving DeepSeek V4 Pro 0813 and 1 provider serving Mistral Large 4, and Mistral's model card states that no Hugging Face Inference Provider deploys Large 4.

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ACCESS BY PLATFORM: MISTRAL STUDIO, LE CHAT, DEEPSEEK APP AND THIRD-PARTY PROVIDERS.

Both companies sell the models mainly through their own APIs. Mistral serves Large 4 from Mistral Studio, with what the October 6 announcement describes as multiple regions worldwide plus a European deployment operated end to end by Mistral, and with self-deployment, private cloud and on-premise options for cybersecurity customers. DeepSeek serves V4 Pro from its API at api.deepseek.com, with OpenAI-format and Anthropic-format endpoints, and in its consumer app and website through Expert Mode.

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Access route	Mistral Large 4	DeepSeek V4 Pro
First-party API	Mistral Studio, mistral-large-4	DeepSeek API, deepseek-v4-pro
Consumer app	Le Chat (Free, Pro $14.99, Team $24.99 per user); per-plan model routing not published	DeepSeek app and web, Expert Mode
API providers (Artificial Analysis count)	1	9
Deployment tooling named by the vendor	vLLM, SGLang, NVIDIA NIM, NeMo AutoModel, TensorRT-LLM	Local deployment guidance on the model card; Codex, Claude Code and OpenCode integrations
European deployment option	Yes, operated by Mistral	Not offered on the pages reviewed

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The Le Chat pricing page lists plan prices and usage multipliers but does not say which plan routes to Large 4, so a Le Chat subscription should not be treated as guaranteed Large 4 access. Developers who want to see how DeepSeek names its current models and legacy aliases can use our guide to DeepSeek API models and model selection.

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BENCHMARK METHODOLOGY AND WHY THE VENDOR TABLES DO NOT LINE UP.

The vendors' own benchmark tables cannot be placed side by side. Mistral's October 29 announcement reports results for the final Large 4 on FrontierCode 2.0, Terminal-Bench 4.0, BrowseComp, FinWorkBench and several cybersecurity tests, while its October 6 page reported preview results on a different set. DeepSeek's readable benchmark tables come from the April preview model card and use Terminal Bench 2.0, SWE-bench Verified, SWE-bench Pro, HLE and others; the August 13 GA release published its benchmark table only as an image, with no scores in the text. The only benchmark both vendors report by the same name is BrowseComp, where Mistral gives 84.5% for the final Large 4 and DeepSeek gives 83.4% for the April V4 Pro preview at Think Max, measured by different teams on different model versions with undisclosed harness details. That gap of 1.1 points says nothing reliable about the current models.

Mistral's preview announcement made the only direct head-to-head claims: it said Large 4 Preview scored 49.8% on a combined Coding Agent Index ahead of DeepSeek V4 Pro 0813, 59.9% on AutomationBench ahead of DeepSeek V4 Pro, and 1,393 Elo on AA-Briefcase ahead of DeepSeek V4 Pro, without printing DeepSeek's figures in the text. These are vendor claims about the retired preview. The comparable evidence comes from Artificial Analysis, which runs both current versions through the same evaluation suite and publishes the results on its model pages.

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ARTIFICIAL ANALYSIS RESULTS FOR MISTRAL LARGE 4 AND DEEPSEEK V4 PRO.

Artificial Analysis Intelligence Index v5 aggregates a suite of evaluations into one score and records the tokens and cost each model needs to complete it. Its model pages, checked on November 3, 2026, give the following figures for the current versions.

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Artificial Analysis measurement	Mistral Large 4 (high)	Mistral Large 4 (medium)	DeepSeek V4 Pro 0813 (max)
Intelligence Index	26	23	23
Intelligence rank	10 of 23	12 of 23	13 of 23
Cost per Intelligence Index task	$2.06	$1.41	$1.77
Output tokens to run the index	110M	73M	120M
Blended price (7:2:1 cache hit, input, output)	$0.40 per 1M	$0.40 per 1M	$0.69 per 1M
Release date recorded	October 29, 2026	October 29, 2026	August 13, 2026

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The results favor Mistral on accuracy and token efficiency. At its default high effort Large 4 scores 3 points above DeepSeek V4 Pro at maximum effort and uses about 8% fewer output tokens. At medium effort it ties DeepSeek's score while using 73M output tokens instead of 120M, about 39% fewer, and costs about 20% less per task. The one figure that runs the other way is the cost per task at high effort, where Artificial Analysis records $2.06 for Large 4 against $1.77 for DeepSeek, despite Large 4's lower blended token price; Artificial Analysis does not break down that per-task figure on the model pages, so the cause cannot be stated from the published data.

Mistral's model card states that Large 4's score of 26 is the highest among Western open-weight models and that leading Chinese open-weight models score higher on the aggregate. DeepSeek V4 Pro 0813 is not one of those leaders on the current index, which places it below Large 4.

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BENCHMARKS ONLY MISTRAL PUBLISHED FOR LARGE 4.

Mistral's October 29 release gives figures for the final model on coding, research, finance and security tests that DeepSeek has not reported for V4 Pro. Several of them are Artificial Analysis indices that Mistral quotes, and the others come from external or private benchmarks run by Mistral or its partners.

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Benchmark (final Large 4, October 29)	Mistral Large 4	Note
FrontierCode 2.0 (Cognition, private)	19.7%	Top score shown by Mistral
Artificial Analysis Coding Agent Index	48%	Tied with GLM-5.3, 47% fewer tokens per task
Terminal-Bench 4.0	41%	Up from 28% for the preview
BrowseComp	84.5%	Up from 75% for an earlier SFT checkpoint
FinWorkBench (Finch)	73.3%	Second among open models behind Kimi K3
Artificial Analysis Cyber Index	51%	Mistral figure for the final model
B3 Agent Security Benchmark	97.1% attack resistance	Up from 93.3% for the preview
Malicious cyber request refusal	96.6% average	Highest among open models per Mistral

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The cybersecurity results stand out because Mistral presents Large 4 as a model for defenders. The preview page reported 82% on a vulnerability reproduce-and-patch test and 93% on Cybench's 40 challenges, and Artificial Analysis gave the preview 82% on CyberGym-E2E-AA in its October 6 analysis. DeepSeek has not published comparable cybersecurity or agent-security results for V4 Pro on the pages reviewed. Readers comparing Large 4 with another open-weight rival can find the same Mistral figures set against Moonshot's model in our comparison of Mistral Large 4 and Kimi K3.1 Pro.

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BENCHMARKS ONLY DEEPSEEK PUBLISHED FOR V4 PRO.

DeepSeek's readable figures come from the April preview model card, which reports V4 Pro at three modes (non-thinking, Think High and Think Max) on reasoning, coding, long-context and agent tests. These numbers describe the preview, not the 0813 version served today, and DeepSeek has not published a readable update for the GA model.

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Benchmark (April preview, Think Max)	DeepSeek V4 Pro	Metric
SWE-bench Verified	80.6	Resolved
SWE-bench Pro	55.4	Resolved
Terminal Bench 2.0	67.9	Accuracy
LiveCodeBench	93.5	Pass@1
Codeforces	3206	Rating
GPQA Diamond	90.1	Pass@1
HLE (no tools)	37.7	Pass@1
MRCR 1M	83.5	MMR
GDPval-AA	1554	Elo
Toolathlon	51.8	Pass@1

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The long-context score is the one most relevant to this comparison, because both models advertise 1M tokens. DeepSeek reports 83.5 on MRCR at 1M tokens and 62.0 on CorpusQA at 1M tokens at Think Max. Mistral has published no long-context retrieval result for Large 4, so the claim that both models use the full million tokens well is supported by evidence only on DeepSeek's side.

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SPEED, LATENCY AND THROUGHPUT MEASUREMENTS.

Artificial Analysis measures DeepSeek V4 Pro 0813 at maximum effort at 82.6 output tokens per second with 1.76 seconds to the first token on DeepSeek's API, and the non-reasoning mode at 87.5 tokens per second with 1.72 seconds to the first token. For the final Mistral Large 4, Artificial Analysis shows no speed or latency measurement and marks the charts "No publicly available API" at the time of checking. The retired preview, measured on Mistral's API, ran at 170.1 tokens per second with 1.97 seconds to the first token; that figure belongs to a different model version and should be read only as an indication of how Mistral serves the family.

Self-hosted throughput depends on hardware. Hugging Face reports about 65 tokens per second for a single request on eight H200 GPUs with the FP8 Large 4 checkpoint, 120 to 138 tokens per second on chat prompts with accepted speculative drafts, and about 480 tokens per second in total across 1,000 concurrent ShareGPT conversations. Mistral says its DSpark speculative decoding gives up to 1.9 times the throughput depending on hardware and workload. DeepSeek publishes no comparable self-hosting throughput figures for V4 Pro on its model card.

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COST OF THREE TYPICAL WORKLOADS ON MISTRAL LARGE 4 AND DEEPSEEK V4 PRO.

Per-token prices only become meaningful when applied to a workload. The table below uses the official rates from November 3, 2026 and three monthly workloads: a support chatbot with 300,000 requests of 2,000 input and 400 output tokens and a 60% cache hit rate; a coding agent with 500 sessions of 2 million input and 150,000 output tokens and an 80% cache hit rate; and a long-document review of 1,000 files of 300,000 input and 5,000 output tokens with no caching. The "time-weighted" DeepSeek column assumes traffic spread evenly across the week, so that 35 of 168 hours (20.8%) fall in peak windows. Reasoning tokens are counted as output tokens, and the same token counts are applied to both models, which leaves out the difference in verbosity measured by Artificial Analysis.

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Monthly workload	Mistral Large 4	DeepSeek V4 Pro off-peak	DeepSeek V4 Pro peak	DeepSeek V4 Pro time-weighted
Support chatbot (600M input, 120M output)	$445.20	$403.92	$807.84	$488.07
Coding agent (1B input, 75M output)	$353.50	$298.10	$596.20	$360.20
Long-document review (300M input, 5M output)	$220.50	$207.90	$415.80	$251.21

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DeepSeek is cheaper only for teams that can move work into its off-peak hours, where it saves 6% to 16% against Mistral; the larger saving on the coding agent comes from DeepSeek's lower cached-input rate. With traffic spread evenly across the week, Mistral Large 4 is cheaper in all three cases, by about 9% on the chatbot, 2% on the coding agent and 12% on document review. Any workload concentrated in European business mornings, which overlap DeepSeek's 06:00 to 10:00 UTC peak, moves closer to the peak column. The Artificial Analysis token counts point in the same direction: if Large 4 at medium effort needs about 39% fewer output tokens than DeepSeek at max for the same score, the output share of every bill above shrinks further on Mistral's side.

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DATA LOCATION, SAFETY TESTING AND CYBER SAFEGUARDS.

The two companies give very different answers on where data goes. DeepSeek's privacy policy, last updated February 10, 2026, states that DeepSeek collects, processes and stores personal data in the People's Republic of China, that user inputs may be used to train and improve its models, and that users have a right to opt out of training use by request. The same policy states that data end users submit to applications built by developers on the DeepSeek open platform is not covered, because the developer acts as controller. Mistral offers a European deployment operated end to end by Mistral, and its open weights let either company's customers keep data on their own servers.

On safety, Mistral has published figures and DeepSeek has not, on the pages reviewed. Mistral reports 97.1% attack resistance on the Lakera B3 Agent Security Benchmark, a 96.6% average refusal rate for malicious cyber requests, and, for the preview, a KORA Benchmark score of 1.691 out of 2. Mistral also describes the context behind its cyber claims: its preview page says Claude Opus 5.5 and GPT-6 Astra score near zero on its reproduce-and-patch test because they refuse the task, which reflects a deliberate choice by Mistral to allow defensive exploitation work. Organizations with strict content policies should test Large 4's behavior on security prompts before deployment.

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WHAT NEITHER VENDOR HAS PUBLISHED FOR THIS COMPARISON.

Several figures a buyer would want are missing. DeepSeek has not published readable benchmark scores for the 0813 version served on its API, so every DeepSeek figure in the vendor tables refers to the April preview. Mistral has not published a maximum output length, a long-context retrieval score, the training data cutoff or the size of its pretraining corpus for Large 4, and its documentation still labels the model as a public preview. Neither company has published a same-harness comparison with the other model, and Artificial Analysis has not yet measured the speed of the final Large 4 on a public API.

The rate-limit picture is also incomplete. DeepSeek gives a concurrency limit of 500 requests for V4 Pro; Mistral sets per-model tokens-per-minute and requests-per-second limits that are visible only in each organization's admin panel. Teams planning high-volume deployments on Mistral need to check those limits in their own account before comparing capacity.

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CHOOSING BETWEEN MISTRAL LARGE 4 AND DEEPSEEK V4 PRO BY WORKLOAD.

The published evidence supports a choice made workload by workload, with each model leading on different requirements.

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Workload or requirement	Recommended model	Reason
Highest aggregate accuracy among the two	Mistral Large 4 (high)	Intelligence Index 26 vs 23
Lowest cost per task at equal accuracy	Mistral Large 4 (medium)	Score 23 at $1.41 per task vs $1.77, 39% fewer output tokens
Image, chart and document input	Mistral Large 4	V4 Pro does not support vision
Batch jobs scheduled at night or weekends	DeepSeek V4 Pro	Off-peak rates 6% below Mistral, cached input under a third
Daytime traffic in European hours	Mistral Large 4	DeepSeek's 06:00-10:00 UTC peak doubles its price
Very long outputs and reasoning traces	DeepSeek V4 Pro	Published ceiling of 384K output tokens
Reselling hosted inference of the weights	DeepSeek V4 Pro	MIT license; Mistral requires a commercial license for MaaS
EU data residency or on-premise security work	Mistral Large 4	Mistral-operated European deployment and published cyber results

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Users moving from Mistral Large 3 face a fixed timeline, since that model was deprecated on October 31, and the launch details of its successor are in our report on the Mistral Large 4 open-weight release. For most developers starting a new integration this month, Mistral Large 4 at medium effort is the better default: it reaches DeepSeek V4 Pro's best Artificial Analysis score with fewer tokens, accepts images, and costs less than DeepSeek whenever traffic touches DeepSeek's peak windows. DeepSeek V4 Pro remains the stronger pick for cost-optimized night and weekend batch processing, for outputs longer than Mistral documents, and for any provider whose business model depends on hosting the weights without a separate license.

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