🔍 Read the full analysis: My October 2026 AI Stack: Opus Builds, Sol Digs, Jev Decides, And Mistral Large 4 Falls Short on ThorstenMeyerAI.com
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TL;DR
ThorstenMeyerAI.com’s October review keeps its existing AI lineup and finds Mistral Large 4 is outscored at a lower estimated task cost by seven model configurations on the Artificial Analysis Intelligence Index. The author also describes Gemini 4 Argon as a possible second opinion, but says restricted access keeps it out of production for now.
ThorstenMeyerAI.com says its October AI stack remains unchanged after testing new models, reporting that Mistral Large 4 is outscored at a lower estimated task cost by seven configurations in the comparison. The review also identifies Gemini 4 Argon as a possible second-opinion model, but says its restricted availability prevents production use for now.
The review assigns different jobs to different models rather than selecting one overall winner. It uses Claude Opus 5.5 at high effort for most building and at xhigh for harder problems, while GPT-6.1 Sol handles detailed analysis and review. A model called Jev handles high-volume yes-or-no and routing decisions; Sonnet 5.5 and GPT-6 Luna cover scoped side tasks. The article gives no further technical or provider information about Jev.
For its comparison, the author cites the Artificial Analysis Intelligence Index v4.3.x, describing it as a general-capability measure rather than a forecast of performance on every reader’s workload. In that comparison, Mistral Large 4 scores 38 and is estimated to cost $1.13 per task. The listed alternatives include GPT-6.1 Sol at medium, which scores 48 at $0.21 per task, and GLM-5.3-Flash, which scores 42 at $0.25 per task.
The article says seven configurations score higher while costing less, including three Sol effort settings, GLM-5.3-Flash, DeepSeek V4.1 Flash, Claude Opus 5.5 at low, and Claude Sonnet 5.5 at medium. It adds that six of the seven still meet that comparison during Mistral’s stated two-week launch discount, when its estimated task cost falls to $0.57. These results and prices are figures reported by the source, not independent verification of performance or costs for a reader’s own applications.
Opus builds, Sol digs, Jev decides — and Mistral Large 4 doesn’t make the cut
Two models landed on the price curve in eight days. My stack doesn’t change. The test is the same as in September: does it clear my bar at a lower cost per task than what I already run? For Mistral Large 4, no — seven configurations beat it on score and cost at once.
Cheaper per token ($4.18 vs Sol’s $10 output) — but ~8× the output of Sol for a lower score. Budget cost per task, not per token.
Speed: 116 tok/s, 1.46 s to first token — far faster than Sol at high/xhigh (57–69 s).
Cyber: AA Cyber Index 50, CyberGym-E2E 82%.
Jurisdiction: French parent, weights at the end of October.
For legally bound buyers, the best European option by a wide margin. For my stack, none of it clears the bar.
Being behind the frontier is normal for a challenger. Being beaten on both axes by models you can already buy is a pricing problem, not a capability one. Argon is the more interesting arrival — level with Astra and Fable at a third of Fable’s cost — but access is still restricted, and a model I can’t put into production isn’t part of my stack. Run the dominance test on every new model before you read its benchmark table. Most new models don’t change anything — the ruler tells you which ones do.
Why Task Costs Change the Ranking
The comparison highlights a practical distinction for teams choosing models: cost per completed task can matter more than the posted cost per token. The source says Mistral Large 4’s token prices appear competitive, but that its Index run produced 200 million output tokens, compared with a median of 81 million for comparable models and 25 million for GPT-6.1 Sol at high. Those figures come from the article’s account of the benchmark run.
That difference could matter more in long-running agent workflows, where repeated output adds expense and errors can carry forward. The author says its own testing found confident hallucinations in Mistral Large 4; this is presented as a personal observation, not an Artificial Analysis result. Readers should treat the ranking as a reason to test cost, accuracy and latency on their own tasks, not as a universal verdict.
The article also reports strengths that complicate a simple ranking. It gives Mistral Large 4 a score of 50 on the AA Cyber Index and an 82% result on CyberGym-E2E, and reports speed of 116 tokens per second with 1.46 seconds to first token. It says the model’s weights are due at the end of October under EU jurisdiction, a feature the author considers relevant to users required to use a European model. The source does not independently establish that it is the best available choice for every such user.
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How the October Models Compare
The article frames its review as a continuation of a price-and-capability comparison it published eight days earlier. It says Gemini 4 Argon arrived September 30 and Mistral Large 4 became available the day before the October assessment. The comparison includes models from US, French and Chinese labs, with two open-weight Chinese models included as reference points.
According to the source, Argon scores 53 on the cited Index and costs about $1.99 per task under introductory pricing. It also reports that Argon has the lowest hallucination rate among models scoring above 45 on the Index, though the article does not supply the underlying rate in its table. Access remains limited to selected users, with general availability pending, according to the review.
The author’s stated test for adding a model is whether it improves the quality-and-cost trade-off for a particular role. On that basis, the review keeps Opus for building, Sol for detailed review, and Jev for frequent routing decisions. It treats Argon as a candidate rather than an active component and says Mistral Large 4 does not meet its own bar.
““The answer is no.””
— ThorstenMeyerAI.com
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Access and Workload Fit Remain Open
The source does not provide independent replication of its benchmark results, detailed methodology for calculating each estimated task cost, or enough information to establish how closely the Index reflects a specific company’s workload. It explicitly warns that the Index measures general capability, not performance on every task, and recommends shadow-testing before switching models.
It is also unclear when Gemini 4 Argon will become generally available. Mistral’s weights are described as due at the end of October, but the article does not confirm that release has occurred. The author’s hallucination observations are limited to personal testing, and the claims about European suitability depend on users’ individual legal and operational requirements.
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Testing Before Any Stack Changes
The author says it will test Gemini 4 Argon if and when public access opens. Until then, the published stack remains unchanged. The article recommends readers run shadow tests against their own prompts and workflows before replacing a model, comparing completed-task quality, output volume, latency and total cost rather than relying only on list prices or a general benchmark.
The next relevant developments are Argon’s availability, the reported end-of-October release of Mistral Large 4’s weights, and whether further testing changes the author’s model assignments. The source provides no confirmed date for Argon’s wider release or a later update to the stack.
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Key Questions
What is the main finding about Mistral Large 4?
The article says seven model configurations score higher on the cited Artificial Analysis Intelligence Index while costing less per estimated task. It also reports that six still do so at Mistral’s stated launch-discount price.
Is Mistral Large 4 being called a poor model overall?
No. The author reports strengths in cyber benchmarks and speed, but says it does not meet the author’s quality-and-cost requirements for the described stack. The review cautions that a general index is not a verdict for every workload.
Why does the review say Mistral’s token prices can be misleading?
It says Mistral Large 4 generated substantially more output tokens in the cited Index run than comparable models. Higher output volume can raise total task costs even when the price per token is relatively low.
Why is Gemini 4 Argon not in the current stack?
The review describes Argon as restricted to selected users, with general availability pending. The author says it will be considered for a second-opinion role once it can be tested and deployed more broadly.
Should readers change models based on this comparison?
The source advises shadow-testing before switching. Its scores and cost estimates are tied to a general benchmark and may not predict results for an individual team’s tasks, usage patterns or requirements.
Source: ThorstenMeyerAI.com
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