🔍 Read the full analysis: A Closer Look At Mistral Large 4’S Global Strengths And Agent Limits on ThorstenMeyerAI.com
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TL;DR
Mistral Large 4 scored 38.4 on Artificial Analysis’s Intelligence Index v4.3.2, a sharp improvement over the company’s previous models but below the current US and Chinese leaders listed in the source. The source argues its price, high token use and reported hallucinations make it a questionable choice for long-running agent tasks; those judgments combine benchmark data with the author’s own testing.
Mistral released Large 4 as a Research Public Preview, and the model scored 38.4 on Artificial Analysis’s Intelligence Index v4.3.2. The result marks a steep improvement over Mistral’s earlier models, but the source’s comparison places Large 4 below the leading US and Chinese systems and raises questions about its price and suitability for agent workflows.
Artificial Analysis’s index score for Large 4 is 38.4. In the comparison provided by ThorstenMeyerAI.com, that is below US models including Claude Opus 5.5 at 57.6 and GPT-6 Astra at 52.7, and below several Chinese models, including GLM-5.3 at 44.8 and DeepSeek V4.1 Flash at 39.5. These are scores on the same cited index version, rather than separate company claims. The source describes Large 4 as the highest-scoring model outside the US and China, while stressing that this framing does not put it at the overall frontier.
The release is a substantial step up within Mistral’s own lineup: the source lists Mistral Large 3 at 9 and Medium 3.5 at 14 on the same index version. Large 4 has one trillion total parameters, with 49 billion active, accepts text and images, produces text, and has a 512,000-token context window. Mistral says reinforcement learning is continuing, so its scores may change.
Large 4 is currently a proprietary API preview. Mistral has promised to release weights by the end of October, but the source says the licence has not been published. Listed API pricing is $1.36 per million input tokens and $4.18 per million output tokens, with cached input at $0.14 per million; the source reports a 50% discount for the first two weeks. The provided comparison estimates $1.13 per Intelligence Index task, but does not specify the task-cost calculation method.
Mistral Large 4: best outside the US and China — and still not a model to run your agents on
The headline is true: France has the most intelligent model outside the US and China. The independent data says the rest: every US and Chinese flagship scores higher, the best by 19 points. It costs 4× more per task than Chinese open models that outscore it, and it’s 2.5× as verbose as the median model.
~two-thirds of Opus 5.5. Level with OpenAI’s small model, Luna.
Eighth among open models once weights ship — behind seven Chinese ones. Beats GLM-5.2 and V4 Pro, loses to their successors.
Cohere doesn’t compete at this tier — reported ~14% hallucination at ~9% accuracy, because it declines most questions. A field of one.
The Index is now agentic-heavy — Briefcase, GDPval, AutomationBench, Terminal-Bench. Errors multiply across steps: tolerable in chat, fatal over a two-hour run.
AA v4.3.2Output tokens to complete the Index. On an agent, verbosity is cost and latency on every step.
AAConfident false assertions in hands-on use. US frontier has largely moved past this — Gemini 4 Argon: 15%. In fairness Chinese open models are worse (Kimi K3 51%, DeepSeek V4 Pro 94%). In an agent, a fabrication is a wrong premise every later step builds on.
AUTHOR’S TESTING · not an AA figure- Cyber defence: 50 on the AA Cyber Index; 82% CyberGym-E2E (ahead of Luna’s 78%). Likely top-3 open model on cyber.
- Documents & images: 19% GDP.pdf (+18 vs Large 3); 100 images per request.
- Speed: 116 tok/s, 1.46s TTFT — well above median.
- The jump: Large 3 scored 9 on this Index. 9 → 38 is real progress.
- Jurisdiction: French parent, EU hosting, weights promised end of October.
- Legally bound buyers (defence, classified, DORA, health data): now the best European option by a wide margin. Wait for the weights, check the licence, pilot on cyber and documents.
- Everyone else, for agentic or long tasks: don’t. A US frontier model is meaningfully more capable; GLM-5.3-Flash is more capable and 4× cheaper.
- Note: Preview — Mistral says RL is still running, so scores may move. That changes next month’s decision, not today’s.
Mistral says it has “essentially closed the gap.” It has closed the gap to where the Chinese open-weights field was a few months ago, while that field and the US frontier have both moved on. On every independent measure that matters for agents — intelligence, cost per task, verbosity and factual reliability — Large 4 is not a frontier model. “Most intelligent outside the US and China” is true mainly because almost nobody else outside those two countries is competing. Use it if you have to. Don’t use it because of the headline.
Price and Reliability Shape Agent Use
The benchmark matters to buyers because Artificial Analysis’s index includes agent-oriented evaluations, such as knowledge work, software workflows and coding tasks. A model’s limitations can have a larger effect when it must carry out multiple steps: an early error can shape later actions, while extra output can add both cost and latency. That is a reason for careful testing, not proof that every deployment will fail.
The source estimates Large 4 costs $1.13 per index task, compared with $0.25 for GLM-5.3-Flash and $0.27 for DeepSeek V4.1 Flash. It reports scores of 41.8 and 39.5 for those models, respectively, both above Large 4’s 38.4. Those figures suggest buyers should compare completed-task costs and performance, not token prices alone. They do not establish costs for every workload or deployment.
The source also says Large 4 used 200 million output tokens to complete the index, against a median of 81 million for comparable models. That is a reported benchmark observation; the source does not provide enough detail here to establish how token use will vary in customer tasks. ThorstenMeyerAI.com separately reports seeing confident false statements in hands-on use. That is an individual observation, not a published Artificial Analysis hallucination score for Large 4, and should be treated accordingly.
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A Jump From Mistral’s Earlier Scores
The release is significant for Mistral because the source’s like-for-like index comparison shows a rise from 9 for Large 3 to 38.4 for Large 4. That indicates rapid progress on this benchmark, though it does not erase the remaining difference from higher-scoring models. The source characterizes the gap to the listed US leader, Claude Opus 5.5, as 19.2 points.
The headline that France has the most intelligent model outside the US and China is attributed to Artificial Analysis’s ranking as described in the source. It is a geographic comparison, not a claim that Large 4 leads all global models. The supplied table lists several Chinese models above it, and the source says Large 4 would place eighth among open-weight models if its weights are released. That standing remains conditional on the promised release and the relevant model set.
For now, access is through Mistral’s API preview, so the system is not yet an open-weight option for developers. The forthcoming weights could affect its appeal to organizations that want to run or adapt models themselves, but their availability, licence terms and final benchmark position were not settled in the source.
“Reinforcement learning is still running.”
— Mistral
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Preview Status Leaves Open Questions
Large 4 is still in Research Public Preview, and the source says Mistral’s reinforcement-learning work is ongoing. Its score and behavior could change. The source does not provide a Large 4-specific hallucination rate, nor does it describe the author’s hands-on testing method, sample size or tasks, so that observation cannot be generalized into a measured failure rate.
The source says Mistral has promised weights by the end of October but has not published a licence. The year is not stated in the supplied material, and it is unclear whether the promised date will hold or what use the licence will permit. The task-cost figures are also estimates whose calculation details are not included, so procurement comparisons should be checked against a buyer’s own workload and pricing.
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Watch for Weights and Retesting
The next milestones are Mistral’s promised weight release at the end of October and any licence announcement. Developers and buyers can then assess whether the release supports their deployment needs, while updated index results may show how ongoing reinforcement learning affects performance.
For organizations considering the API preview now, the practical next step is to test representative tasks and measure accuracy, completion rates, token use and total cost. The supplied results point to meaningful progress, but they do not establish that Large 4 is the best fit for a particular workflow.
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Key Questions
How did Mistral Large 4 score?
It scored 38.4 on Artificial Analysis’s Intelligence Index v4.3.2, according to the source. The source lists several US and Chinese models with higher scores.
Is Mistral Large 4 open source or open weight now?
No. The source describes it as a proprietary API preview. Mistral has promised to release weights by the end of October, but the licence was unpublished in the report.
What does the source say about agent use?
It questions Large 4’s fit for long-running agent tasks, citing its index score, reported output-token use and the author’s observation of confident false statements. The hallucination observation is not a published Large 4 benchmark rate.
How much does the API cost?
The listed standard rates are $1.36 per million input tokens, $4.18 per million output tokens and $0.14 per million cached input tokens. The source also reports a 50% discount for the first two weeks.
Source: ThorstenMeyerAI.com
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