GPT-5.3-Codex vs Mistral Medium 3.1
Updated October 21, 2026. 17 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.
Mistral Medium 3.1 has the edge where output tokens cost 7.0x less, and where the benchmark that matters is Design Arena: 3D, where it leads.
- Shared results
- 17
- GPT-5.3-Codex only
- 17
- Mistral Medium 3.1 only
- 57
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 GPT-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Math
AIME
No call
Only Mistral Medium 3.1 has a AIME result, 42.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.1 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 1133.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.1
GPT-5.3-Codex $0.0088, Mistral Medium 3.1 $0.0014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Mistral Medium 3.1 not benchmarked.
- Widest category
- Agentic work, 45.3 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-5.3-Codex 1655.2 against Mistral Medium 3.1 210.2, 48.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Mistral Medium 3.1 $2.00 per million output tokens, 7.0x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Mistral Medium 3.1 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-5.3-Codex | Bars | Mistral Medium 3.1 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 7 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | 87.3% | 44.8% | GPT-5.3-Codex |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| SciCode | Not measured | 32.4% | Only Mistral Medium 3.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 10.6% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | 210.2 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1433 | Only Mistral Medium 3.1 measured |
| LMArena Text: software and IT | Not measured | 1436 | Only Mistral Medium 3.1 measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 9.2 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | 40.6% | GPT-5.3-Codex |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | Not measured | 63.0% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Artificial Analysis run) | 91.5% | 58.8% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 4.7% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1426 | Only Mistral Medium 3.1 measured |
| LMArena Text: expert | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: hard prompts in English | Not measured | 1435 | Only Mistral Medium 3.1 measured |
| LMArena Vision: diagrams | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 42.3% | Only Mistral Medium 3.1 measured |
| MATH 500 (Vals AI) | Not measured | 87.0% | Only Mistral Medium 3.1 measured |
| MGSM (Vals AI) | Not measured | 90.9% | Only Mistral Medium 3.1 measured |
| AA Math Index | Not measured | 38.3 | Only Mistral Medium 3.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 38.3% | Only Mistral Medium 3.1 measured |
| LMArena Text: math | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: mathematical work | Not measured | 1412 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.1 measured |
| LegalBench (Vals AI) | Not measured | 62.0% | Only Mistral Medium 3.1 measured |
| CorpFin (Vals AI) | Not measured | 50.7% | Only Mistral Medium 3.1 measured |
| TaxEval (Vals AI) | Not measured | 70.3% | Only Mistral Medium 3.1 measured |
| MortgageTax (Vals AI) | Not measured | 36.5% | Only Mistral Medium 3.1 measured |
| MedQA (Vals AI) | Not measured | 78.2% | Only Mistral Medium 3.1 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Medium 3.1 measured |
| LMArena Text: science | Not measured | 1444 | Only Mistral Medium 3.1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1449 | Only Mistral Medium 3.1 measured |
| LMArena Text: legal and government | Not measured | 1439 | Only Mistral Medium 3.1 measured |
| LMArena Text: business and finance | Not measured | 1418 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1133 | GPT-5.3-Codex |
| Design Arena: UI components | 1137 | 1099 | GPT-5.3-Codex |
| Design Arena: game dev | 1161 | 1074 | GPT-5.3-Codex |
| Design Arena: data viz | 1163 | 1144 | GPT-5.3-Codex |
| Design Arena: 3D | 1008 | 1081 | Mistral Medium 3.1 |
| Design Arena: all code categories | 1145 | 1120 | GPT-5.3-Codex |
| Design Arena: SVG | 1129 | 993 | GPT-5.3-Codex |
| Design Arena: ASCII art | 1151 | 997 | GPT-5.3-Codex |
| 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-5.3-Codex | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| IFBench | 75.4% | 39.8% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 83.3% | 42.7% | GPT-5.3-Codex |
| DTBench | Not measured | 67.5% | Only Mistral Medium 3.1 measured |
| LMCA | Not measured | 19.1% | Only Mistral Medium 3.1 measured |
| LMArena Text | Not measured | 1424 | Only Mistral Medium 3.1 measured |
| LMArena Text: instruction following | Not measured | 1398 | Only Mistral Medium 3.1 measured |
| LMArena Text: creative writing | Not measured | 1391 | Only Mistral Medium 3.1 measured |
| LMArena Vision | Not measured | 1172 | Only Mistral Medium 3.1 measured |
| LMArena Text: English | Not measured | 1438 | Only Mistral Medium 3.1 measured |
| LMArena Text: non-English | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: Chinese | Not measured | 1447 | Only Mistral Medium 3.1 measured |
| LMArena Text: German | Not measured | 1432 | Only Mistral Medium 3.1 measured |
| LMArena Text: Japanese | Not measured | 1379 | Only Mistral Medium 3.1 measured |
| LMArena Text: Korean | Not measured | 1380 | Only Mistral Medium 3.1 measured |
| LMArena Text: French | Not measured | 1460 | Only Mistral Medium 3.1 measured |
| LMArena Text: Spanish | Not measured | 1433 | Only Mistral Medium 3.1 measured |
| LMArena Text: Russian | Not measured | 1411 | Only Mistral Medium 3.1 measured |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.1 measured |
| LMArena Text: multi-turn | Not measured | 1418 | Only Mistral Medium 3.1 measured |
| LMArena Text: longer queries | Not measured | 1406 | Only Mistral Medium 3.1 measured |
| LMArena Text: writing and language | Not measured | 1395 | Only Mistral Medium 3.1 measured |
| LMArena Text: entertainment and media | Not measured | 1390 | Only Mistral Medium 3.1 measured |
| LMArena Vision: English | Not measured | 1179 | Only Mistral Medium 3.1 measured |
| LMArena Vision: Chinese | Not measured | 1190 | Only Mistral Medium 3.1 measured |
| LMArena Vision: reading text | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | Not measured | 1200 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | Mistral Medium 3.1 | 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 3.1 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.1 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.1 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-5.3-Codex | Mistral Medium 3.1 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | February 24, 2026 | August 13, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.75 | $0.40 |
| Output $/Mtok | $14.00 | $2.00 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Mistral Medium 3.1 for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Mistral Medium 3.1 has the edge where output tokens cost 7.0x less, and where the benchmark that matters is Design Arena: 3D, where it leads.
Which is cheaper, GPT-5.3-Codex or Mistral Medium 3.1?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Mistral Medium 3.1?
GPT-5.3-Codex, with 400K tokens against 131K.
How many benchmarks is the GPT-5.3-Codex vs Mistral Medium 3.1 comparison based on?
17 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.