Intelligence
15 scoreMistral · Flagships Analysis
Mistral Medium 3.1
Intelligence, Performance & Price Analysis
Canonical slug: mistral-medium-3-1 · Canonical model registry at build time
Speed
88.6 output tokens/secLatency
1.59s TTFTInput Price
$0.40 / 1M tokensOutput Price
$2.00 / 1M tokensVerbosity
8.6M Output tokens from Intelligence Index 3 out of 4 units for Verbosity . CompExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Capable everyday model
Mistral Medium 3.1 scores 15 on the Artificial Analysis Intelligence Index, placing it above average among other non-reasoning models in a similar price tier (median: 11). Mistral Medium 3.1 generates output at 88.6 tokens per second (based on Mistral's API), which is below average compared to other non-reasoning models in a similar price tier (median: 95.5 t/s). Mistral Medium 3.1 costs $0.40 per 1M input tokens (somewhat higher than average, median: $0.20) and $2.00 per 1M output tokens (at the higher end, median: $0.70), based on Mistral's API.
Good for routine tasks; route complex reasoning and premium workloads to stronger models.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | viable | Intelligence score 15 handles routine reasoning but may struggle with open-ended agentic tasks. |
| High-volume chat & customer-facing | optimal | Output speed 88.6 tokens/sec is adequate for chat. |
| Latency-sensitive applications | optimal | TTFT 1.59s — adequate latency for most interactive use cases. |
| Cost-sensitive pipelines | viable | Output pricing at $2.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
Medium
Pricing is moderate — input $0.40, output $2.00. Costs accumulate at volume but are manageable for valuable tasks.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | No | faq |
| Input modalities | Mistral Medium 3.1 supports text and image input. | faq |
| Output modalities | Mistral Medium 3.1 supports text output. | faq |
| Context window | 130k tokens | faq |
| Open weights / source | No, Mistral Medium 3.1 is proprietary. The model weights are not publicly available. | faq |
| Parameters | Mistral Medium 3.1 is a proprietary model and Mistral has not disclosed the model size or parameter count. | faq |
| API availability | Yes, Mistral Medium 3.1 is available via API through 1 provider. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Mistral Medium 3.1.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Mistral Medium 3.1 was released on August 12, 2025.
Mistral Medium 3.1 was created by Mistral.
Mistral Medium 3.1 scores 15 on the Artificial Analysis Intelligence Index, placing it above average among other non-reasoning models in a similar price tier (median: 11).
Mistral Medium 3.1 generates output at 88.6 tokens per second (based on Mistral's API), which is below average compared to other non-reasoning models in a similar price tier (median: 95.5 t/s).
Mistral Medium 3.1 has a time to first token (TTFT) of 1.59s (based on Mistral's API), which is better than average compared to other non-reasoning models in a similar price tier (median: 1.53s).
Mistral Medium 3.1 costs $0.40 per 1M input tokens (somewhat higher than average, median: $0.20) and $2.00 per 1M output tokens (at the higher end, median: $0.70), based on Mistral's API.
Mistral Medium 3.1 costs $0.40 per 1M input tokens and $2.00 per 1M output tokens (based on Mistral's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.56 per 1M tokens. Pricing may vary by provider.
When evaluated on the Intelligence Index, Mistral Medium 3.1 generated 8.6M output tokens, which is somewhat higher than average compared to other non-reasoning models in a similar price tier (median: 8.4M).
No, Mistral Medium 3.1 is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Mistral Medium 3.1 supports text and image input.
Mistral Medium 3.1 supports text output.
Yes, Mistral Medium 3.1 supports image input and can analyze, describe, and answer questions about images.
Yes, Mistral Medium 3.1 is multimodal. It can process text and image input and generate text output.
Mistral Medium 3.1 has a context window of 130k tokens. This determines how much text and conversation history the model can process in a single request.
No, Mistral Medium 3.1 is proprietary. The model weights are not publicly available.
Mistral Medium 3.1 is a proprietary model and Mistral has not disclosed the model size or parameter count.
Mistral Medium 3.1 achieves a score of 15 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, Mistral Medium 3.1 is available via API through 1 provider.
Mistral Medium 3.1 is available through 1 API provider.