GPT-5.1-Codex-Max vs Mistral Medium 3.5
Mistral Medium 3.5 costs 1.3x less per output token than GPT-5.1-Codex-Max ($7.50 vs $10.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GPT-5.1-Codex-Max or Mistral Medium 3.5: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens) and work measured by Vibe Code Bench (Vals AI) or ALE-Bench, where it leads.
- Shared results
- 3
- GPT-5.1-Codex-Max only
- 3
- Mistral Medium 3.5 only
- 78
- Like-for-like categories
- 2 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Medium 3.5 has a AA Intelligence Index result, 14.2.
Directional only
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Mistral Medium 3.5 has a GPQA Diamond result, 34.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.5 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.5
GPT-5.1-Codex-Max $0.0063, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Coding, 17.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.1-Codex-Max 22.2% against Mistral Medium 3.5 2.9%, 19.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Mistral Medium 3.5 $7.50 per million output tokens, 1.3x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Mistral Medium 3.5 262K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.1-Codex-Max | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Math | 0 | 0 | 1 tie | |
| All shared boards | 2 | 0 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | Not measured | 46.9 | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| SWE-bench Verified (Vals AI) | Not measured | 66.4% | Only Mistral Medium 3.5 measured |
| Vibe Code Bench (Vals AI) | 22.2% | 2.9% | GPT-5.1-Codex-Max |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | 764.0 | GPT-5.1-Codex-Max |
| LMArena Text: coding | Not measured | 1460 | Only Mistral Medium 3.5 measured |
| LMArena Text: software and IT | Not measured | 1458 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 14.2 | Only Mistral Medium 3.5 measured |
| Epoch Capabilities Index | Not measured | 141.4 | Only Mistral Medium 3.5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 34.9% | Only Mistral Medium 3.5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 74.8% | Only Mistral Medium 3.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 13.8% | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts | Not measured | 1436 | Only Mistral Medium 3.5 measured |
| LMArena Text: expert | Not measured | 1432 | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1445 | Only Mistral Medium 3.5 measured |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | Not measured | 41.4% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index00
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| ProofBench | 9.0% | 9.0% | Tie |
| LMArena Text: math | Not measured | 1431 | Only Mistral Medium 3.5 measured |
| LMArena Text: mathematical work | Not measured | 1440 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.5 measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | Not measured | 58.8% | Only Mistral Medium 3.5 measured |
| TaxEval (Vals AI) | Not measured | 68.0% | Only Mistral Medium 3.5 measured |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 measured |
| LMArena Text: science | Not measured | 1428 | Only Mistral Medium 3.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1409 | Only Mistral Medium 3.5 measured |
| LMArena Text: legal and government | Not measured | 1437 | Only Mistral Medium 3.5 measured |
| LMArena Text: business and finance | Not measured | 1426 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1142 | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | Not measured | 1120 | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | Not measured | 1061 | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | Not measured | 1121 | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | Not measured | 950 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1063 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | Not measured | 67.7% | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | Not measured | 68.8% | Only Mistral Medium 3.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only Mistral Medium 3.5 measured |
| DTBench | Not measured | 75.5% | Only Mistral Medium 3.5 measured |
| LMCA | Not measured | 26.1% | Only Mistral Medium 3.5 measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | Not measured | 1421 | Only Mistral Medium 3.5 measured |
| LMArena Text: instruction following | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: creative writing | Not measured | 1374 | Only Mistral Medium 3.5 measured |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | Not measured | 1433 | Only Mistral Medium 3.5 measured |
| LMArena Text: non-English | Not measured | 1404 | Only Mistral Medium 3.5 measured |
| LMArena Text: Chinese | Not measured | 1444 | Only Mistral Medium 3.5 measured |
| LMArena Text: German | Not measured | 1453 | Only Mistral Medium 3.5 measured |
| LMArena Text: Korean | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: French | Not measured | 1448 | Only Mistral Medium 3.5 measured |
| LMArena Text: Spanish | Not measured | 1411 | Only Mistral Medium 3.5 measured |
| LMArena Text: Russian | Not measured | 1395 | Only Mistral Medium 3.5 measured |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | Not measured | 1423 | Only Mistral Medium 3.5 measured |
| LMArena Text: longer queries | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: writing and language | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: entertainment and media | Not measured | 1382 | Only Mistral Medium 3.5 measured |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Medium 3.5 costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Max costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Max costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.1-Codex-Max | Mistral Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 4, 2025 | April 30, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $1.50 |
| Output $/Mtok | $10.00 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 262K tokens) and work measured by Vibe Code Bench (Vals AI) or ALE-Bench, where it leads.
Which is cheaper, GPT-5.1-Codex-Max or Mistral Medium 3.5?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, GPT-5.1-Codex-Max or Mistral Medium 3.5?
GPT-5.1-Codex-Max, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Max vs Mistral Medium 3.5 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.