GPT-5.1-Codex-Mini vs Mistral Medium 3
GPT-5.1-Codex-Mini leads Mistral Medium 3 on the Artificial Analysis Intelligence Index, 20.4 to 9.0, at about the same output price ($2.00 vs $2.00 per million). Updated October 21, 2026; 17 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Mistral Medium 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for a larger context window (400K against 131K tokens) and work measured by Design Arena: 3D, where it leads.
- Shared results
- 17
- GPT-5.1-Codex-Mini only
- 5
- Mistral Medium 3 only
- 40
- Like-for-like categories
- 8 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 9.0.
Same board, both measured
Web design
Design Arena: websites
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 1111 against 1079.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini $0.0013, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- Math, 61.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: GPT-5.1-Codex-Mini 91.7 against Mistral Medium 3 30.3, 61.4 points apart on a 0 to 100 scale.
- Output price
- Both cost $2.00 per million output tokens.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Mistral Medium 3 131K.
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-Mini | Bars | Mistral Medium 3 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| Web design | 5 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | 40.0% | GPT-5.1-Codex-Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 3.8% | GPT-5.1-Codex-Mini |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena Text: coding | Not measured | 1385 | Only Mistral Medium 3 measured |
| LMArena Text: software and IT | Not measured | 1385 | Only Mistral Medium 3 measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 9.0 | GPT-5.1-Codex-Mini |
| Epoch Capabilities Index | Not measured | 134.0 | Only Mistral Medium 3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 24.3% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 57.8% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 4.1% | GPT-5.1-Codex-Mini |
| LMArena Text: hard prompts | Not measured | 1364 | Only Mistral Medium 3 measured |
| LMArena Text: expert | Not measured | 1342 | Only Mistral Medium 3 measured |
| LMArena Text: hard prompts in English | Not measured | 1374 | Only Mistral Medium 3 measured |
| LMArena Vision: diagrams | Not measured | 1155 | Only Mistral Medium 3 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | 30.3 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 91.7% | 30.3% | GPT-5.1-Codex-Mini |
| LMArena Text: math | Not measured | 1353 | Only Mistral Medium 3 measured |
| LMArena Text: mathematical work | Not measured | 1354 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | 76.0% | GPT-5.1-Codex-Mini |
| LMArena Text: science | Not measured | 1381 | Only Mistral Medium 3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1381 | Only Mistral Medium 3 measured |
| LMArena Text: legal and government | Not measured | 1384 | Only Mistral Medium 3 measured |
| LMArena Text: business and finance | Not measured | 1352 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | 1079 | GPT-5.1-Codex-Mini |
| Design Arena: UI components | 1081 | 1024 | GPT-5.1-Codex-Mini |
| Design Arena: game dev | 1092 | 1018 | GPT-5.1-Codex-Mini |
| Design Arena: data viz | 1097 | 1034 | GPT-5.1-Codex-Mini |
| Design Arena: 3D | 988 | 1084 | Mistral Medium 3 |
| Design Arena: all code categories | 1094 | 1069 | GPT-5.1-Codex-Mini |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | Leader |
|---|---|---|---|
| IFBench | 67.9% | 39.3% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 31.3% | GPT-5.1-Codex-Mini |
| DTBench | Not measured | 62.3% | Only Mistral Medium 3 measured |
| LMArena Text | Not measured | 1370 | Only Mistral Medium 3 measured |
| LMArena Text: instruction following | Not measured | 1338 | Only Mistral Medium 3 measured |
| LMArena Text: creative writing | Not measured | 1343 | Only Mistral Medium 3 measured |
| LMArena Vision | Not measured | 1157 | Only Mistral Medium 3 measured |
| LMArena Text: English | Not measured | 1385 | Only Mistral Medium 3 measured |
| LMArena Text: non-English | Not measured | 1353 | Only Mistral Medium 3 measured |
| LMArena Text: Chinese | Not measured | 1374 | Only Mistral Medium 3 measured |
| LMArena Text: German | Not measured | 1377 | Only Mistral Medium 3 measured |
| LMArena Text: Japanese | Not measured | 1316 | Only Mistral Medium 3 measured |
| LMArena Text: Korean | Not measured | 1304 | Only Mistral Medium 3 measured |
| LMArena Text: French | Not measured | 1368 | Only Mistral Medium 3 measured |
| LMArena Text: Spanish | Not measured | 1364 | Only Mistral Medium 3 measured |
| LMArena Text: Russian | Not measured | 1359 | Only Mistral Medium 3 measured |
| LMArena Text: Polish | Not measured | 1371 | Only Mistral Medium 3 measured |
| LMArena Text: multi-turn | Not measured | 1384 | Only Mistral Medium 3 measured |
| LMArena Text: longer queries | Not measured | 1360 | Only Mistral Medium 3 measured |
| LMArena Text: writing and language | Not measured | 1352 | Only Mistral Medium 3 measured |
| LMArena Text: entertainment and media | Not measured | 1335 | Only Mistral Medium 3 measured |
| LMArena Vision: English | Not measured | 1172 | Only Mistral Medium 3 measured |
| LMArena Vision: Chinese | Not measured | 1161 | Only Mistral Medium 3 measured |
| LMArena Vision: reading text | Not measured | 1154 | Only Mistral Medium 3 measured |
| LMArena Vision: humor | Not measured | 1145 | Only Mistral Medium 3 measured |
| LMArena Vision: captioning | Not measured | 1094 | Only Mistral Medium 3 measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only Mistral Medium 3 measured |
| LMArena Vision: creative writing | Not measured | 1146 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Mistral Medium 3 | 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
GPT-5.1-Codex-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Mini 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-Mini | Mistral Medium 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | May 7, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $0.25 | $0.40 |
| Output $/Mtok | $2.00 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than Mistral Medium 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for a larger context window (400K against 131K tokens) and work measured by Design Arena: 3D, where it leads.
Which is cheaper, GPT-5.1-Codex-Mini or Mistral Medium 3?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Mistral Medium 3 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Mistral Medium 3?
GPT-5.1-Codex-Mini, with 400K tokens against 131K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Mistral Medium 3 comparison based on?
17 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Mistral Medium 3's on 0. Where only one model has a result, the page says so instead of naming a winner.