Mistral Large 4 vs GPT-6.1 Sol: 2026 comparison

Mistral Large 4 and GPT-6.1 Sol in brief

Mistral Large 4 and GPT-6.1 Sol address the same broad requirement: working through long inputs, reasoning and calling tools within AI applications. Both accept text and images, and both offer roughly one million tokens of context. The clearest differences concern token pricing, maximum output length and the evaluation results supplied. Mistral costs less per token and allows longer outputs. GPT leads on all four shared benchmarks, while also explicitly listing files among its supported input types.

Mistral Large 4 : Mistral Large 4 (Mistral AI): $1.36 / $4.18 per million tokens, 1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

GPT-6.1 Sol : GPT-6.1 Sol (OpenAI): $2 / $10 per million tokens, 1.1M context, benchmarks, verdict and alternatives. Checked October 25, 2026.

They are often weighed against each other by people who hesitate. This page lines up their features point by point (11 criteria), with no partnership with either publisher.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Capabilities (Epoch AI index)

Epoch AI's capabilities index rolls many benchmarks into one number: the higher, the better the model does on hard tasks. Mistral Large 4 : 150. GPT-6.1 Sol : 168.4. Edge: GPT-6.1 Sol.

Shared benchmarks

On the benchmarks both models took, we count each one's wins. Mistral Large 4 : 0 of 4. GPT-6.1 Sol : 4 of 4. Edge: GPT-6.1 Sol.

Input price

The price of tokens sent to the model weighs heavily as soon as you send long documents or chat history. Mistral Large 4 : $1.36 / M tokens. GPT-6.1 Sol : $2 / M tokens. Edge: Mistral Large 4.

Output price

Generated tokens usually cost several times more; they dominate the bill for writing and code. Mistral Large 4 : $4.18 / M tokens. GPT-6.1 Sol : $10 / M tokens. Edge: Mistral Large 4.

Cost of a typical task

For a request of 2,000 input and 500 output tokens, repeated 30,000 times a month (1,000 a day). Mistral Large 4 : $144 per month. GPT-6.1 Sol : $270 per month. Edge: Mistral Large 4.

Context window

The context window sets how much text the model reads at once: useful for long documents and agents. Mistral Large 4 : 1M tokens. GPT-6.1 Sol : 1.1M tokens. Edge: GPT-6.1 Sol.

Max output

The maximum answer length matters to generate long code files or reports. Mistral Large 4 : 262.1K tokens. GPT-6.1 Sol : 128K tokens. Edge: Mistral Large 4.

Accepted inputs

Text, images, files, audio: the more formats accepted, the more cases covered without extra tools. Mistral Large 4 : 2. GPT-6.1 Sol : 3. Edge: GPT-6.1 Sol.

Tool calling

Essential for an agent: the model can call functions, APIs or MCP servers. Mistral Large 4 : yes. GPT-6.1 Sol : yes. A tie on this point.

Open weights

Downloadable weights let you run the model yourself, without sending data to a provider. Mistral Large 4 : no. GPT-6.1 Sol : no. A tie on this point.

Release date

A newer model often has fresher data and better training techniques. Mistral Large 4 : October 6, 2026. GPT-6.1 Sol : September 29, 2026. Edge: Mistral Large 4.

In charts

Our analysis

Released on October 6, 2026 by France's Mistral AI, Mistral Large 4 supports reasoning and tool use alongside text and image inputs. OpenAI's GPT-6.1 Sol, released in the United States on September 29, 2026, covers the same broad capabilities and also lists file inputs. These specifications suggest different priorities rather than a universal winner. Mistral may suit projects where generation volume and output length drive the decision. GPT may appeal more when the supplied evaluations resemble the work involved. The records do not identify either product's intended commercial audience, so these audience distinctions are interpretations of the available specifications.

GPT records an Epoch capability index of 168.37, compared with Mistral's 150.03. Its GPQA result is 95.4%, against 88.1%, and its FrontierMath result is 93.7%, against 58.6%. AIME scores are 100% and 98.3%, while SimpleQA scores are 73.9% and 20%, respectively. GPT also has ARC1 and ARC2 results, but no corresponding Mistral figures are supplied. Mistral charges $1.36 per million input tokens and $4.18 per million output tokens. GPT's corresponding prices are $2 and $10. Your actual spending therefore depends on both input volume and how much text you ask the model to generate.

Neither model has open weights, and neither record specifies a license. The supplied information therefore does not establish redistribution rights or detailed usage terms. It also says nothing about request retention, whether submitted content may be used for training, or where processing takes place. An editor's country alone cannot answer those questions. GPT explicitly supports files as well as text and images, whereas Mistral lists text and images only. That distinction describes declared input support; it does not establish how documents are protected. If your work involves sensitive material, the relevant handling conditions remain unresolved by these records.

Mistral provides a context window of 1,048,576 tokens and a maximum output of 262,144 tokens. GPT offers 1,050,000 tokens of context and up to 128,000 output tokens. Their input capacity is therefore very similar, while Mistral has more room for a long generated response. Both support reasoning and tool calls, but the records do not describe specific integrations or execution behavior. Latency and throughput limits are also absent. In practice, compare your expected response length, input formats and tool requirements with these specifications. Then check performance on representative tasks, since the supplied benchmarks do not resolve every everyday use case.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (Mistral Large 4 value / GPT-6.1 Sol value).

High request volume

Here, Mistral Large 4 has the edge: it wins 3 of the 3 criteria that matter in this situation, namely cost of a typical task ($144 per month / $270 per month), input price ($1.36 / M tokens / $2 / M tokens), and output price ($4.18 / M tokens / $10 / M tokens).

Hard tasks (reasoning, code)

Here, GPT-6.1 Sol has the edge: it wins 2 of the 3 criteria that matter in this situation, namely capabilities (epoch ai index) (150 / 168.4), shared benchmarks (0 of 4 / 4 of 4), and max output (262.1K tokens / 128K tokens).

Agents and long documents

Here, GPT-6.1 Sol has the edge: it wins 2 of the 3 criteria that matter in this situation, namely tool calling (yes / yes), context window (1M tokens / 1.1M tokens), and accepted inputs (2 / 3).

Maximum privacy

Neither stands out on the criteria for this situation (open weights (no / no)): decide with a trial.

Which one to choose?

Choose Mistral Large 4 if…

• you value input price ($1.36 / M tokens)
• you value output price ($4.18 / M tokens)
• you value cost of a typical task ($144 per month)
• you value max output (262.1K tokens)
• you value release date (October 6, 2026)

Choose Mistral Large 4 if token spending is a major constraint, particularly when your workloads generate substantial output. Its higher output ceiling also makes it a logical candidate for long responses. The tradeoff is lower performance on the shared benchmarks supplied here, which you should weigh against the actual accuracy and reasoning demands of your tasks.

Choose GPT-6.1 Sol if…

• you value capabilities (epoch ai index) (168.4)
• you value shared benchmarks (4 of 4)
• you value context window (1.1M tokens)
• you value accepted inputs (3)

GPT-6.1 Sol is the more logical option when the supplied evaluation results carry substantial weight in your decision or when explicitly supported file inputs matter. Its advantages are particularly visible on FrontierMath and SimpleQA. You would be accepting higher input and output token prices, together with a smaller maximum output allowance than Mistral provides.

Strengths and limits of each

Mistral Large 4

• + Very large context (1M tokens): long documents or a whole codebase fit in one request.
• + Agent-ready: tool calling and structured JSON output.

GPT-6.1 Sol

• + Among the top 20% of models according to Epoch AI (ECI 168.4).
• + Very large context (1.1M tokens): long documents or a whole codebase fit in one request.
• + Agent-ready: tool calling and structured JSON output.
• − Expensive: costlier than 84 % of paid models.

The best test is still yours: take three real tasks from your day and give them to both for a week. The price calculator works out the gap on your own volume.

What about the others?

If neither suits you, look at Claude Opus 5.5, GPT-6 Astra, Claude Fable 5.1, Gemini 4 Argon, Claude Sonnet 5.5, and Claude Opus 5.

Related comparisons:

• Claude Opus 5.5 vs GPT-6.1 Sol
• Gemini 4 Argon vs GPT-6.1 Sol
• DeepSeek V4.1 Flash vs GPT-6.1 Sol

Conclusion

Mistral Large 4 offers lower token prices and a larger output allowance. GPT-6.1 Sol has stronger results across the shared evaluations and explicitly supports file inputs. Your decision should reflect which differences affect your work most. Test representative tasks before treating benchmark advantages as practical gains. Privacy conditions also require separate verification, because the supplied records do not describe how either service handles your content.