GPT-5 vs Mistral Small 4
GPT-5 leads Mistral Small 4 on the Artificial Analysis Intelligence Index, 23.0 to 11.3, and Mistral Small 4 costs 16.7x less per output token ($0.60 vs $10.00 per million). Updated October 21, 2026; 13 benchmarks have a result for both.
GPT-5 or Mistral Small 4: which to pick
Only GPT-5 has a coding score, 38.8 on the Vibe Coding Index; Mistral Small 4 has none, so the two cannot be ranked on coding.
Pick Mistral Small 4 for output tokens 16.7x cheaper ($0.60 vs $10.00 per million).
- Shared results
- 13
- GPT-5 only
- 100
- Mistral Small 4 only
- 2
- Like-for-like categories
- 5 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 GPT-5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5
GPT-5 leads, 23.0 against 11.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Small 4 has a AA Agentic Index result, 0.8.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5 has a Design Arena: websites result, 1184.
Directional only
Long documents
Listed context window
GPT-5
GPT-5 takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 4
GPT-5 $0.0063, Mistral Small 4 $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 has the number, $0.39.
Directional only
At a glance
- Coding index
- GPT-5 38.8 from 3 benchmarks, Mistral Small 4 not benchmarked.
- Widest category
- Agentic work, 45.3 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5 86.5% against Mistral Small 4 41.2%, 45.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5 $10.00 against Mistral Small 4 $0.60 per million output tokens, 16.7x apart.
- Context window
- GPT-5 takes 400K tokens, Mistral Small 4 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 | Bars | Mistral Small 4 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| General | 4 | 0 | ||
| All shared boards | 13 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| Aider polyglot | 88.0% | Not measured | Only GPT-5 measured |
| SWE-bench Verified | 74.4% | Not measured | Only GPT-5 measured |
| LMArena WebDev | 1418 | Not measured | Only GPT-5 measured |
| AA Coding Index | 37.8 | 26.6 | GPT-5 |
| LiveCodeBench (Vals AI) | 85.9% | Not measured | Only GPT-5 measured |
| SWE-bench Verified (Vals AI) | 69.0% | Not measured | Only GPT-5 measured |
| Vibe Code Bench (Vals AI) | 20.1% | 0.0% | GPT-5 |
| LiveCodeBench (Artificial Analysis run) | 84.6% | Not measured | Only GPT-5 measured |
| SciCode | Not measured | 38.8% | Only Mistral Small 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | 17.4% | GPT-5 |
| Terminal-Bench | 49.6% | Not measured | Only GPT-5 measured |
| GSO-Bench | 6.9% | Not measured | Only GPT-5 measured |
| WeirdML | 60.7% | Not measured | Only GPT-5 measured |
| ALE-Bench | 807.6 | 497.6 | GPT-5 |
| LMArena Text: coding | 1436 | Not measured | Only GPT-5 measured |
| LMArena Text: software and IT | 1420 | Not measured | Only GPT-5 measured |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5 measured |
| SciCode | 0.0% | Not measured | Only GPT-5 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 | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.0 | 11.3 | GPT-5 |
| Epoch Capabilities Index | 150.0 | Not measured | Only GPT-5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.8 | Only Mistral Small 4 measured |
| τ²-Bench Telecom | 86.5% | 41.2% | GPT-5 |
| METR time horizons | 69.6% | Not measured | Only GPT-5 measured |
| Remote Labor Index | 1.7% | Not measured | Only GPT-5 measured |
| GDPval | 34.8% | Not measured | Only GPT-5 measured |
| BALROG | 32.8% | Not measured | Only GPT-5 measured |
| DeepResearch Bench | 49.6% | Not measured | Only GPT-5 measured |
| BrowseComp-Plus | 80.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GPT-5 measured |
| MMMU Pro (Vals AI) | 81.5% | Not measured | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 85.4% | 76.9% | GPT-5 |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 9.9% | GPT-5 |
| GPQA Diamond (Epoch AI run) | 86.2% | Not measured | Only GPT-5 measured |
| ARC-AGI | 65.7% | Not measured | Only GPT-5 measured |
| ARC-AGI-2 | 9.9% | Not measured | Only GPT-5 measured |
| SimpleBench | 56.7% | Not measured | Only GPT-5 measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-5 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5 measured |
| EBR-bench | 12.7% | Not measured | Only GPT-5 measured |
| VPCT | 66.0% | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only GPT-5 measured |
| LMArena Text: expert | 1419 | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts in English | 1419 | Not measured | Only GPT-5 measured |
| LMArena Vision: diagrams | 1252 | Not measured | Only GPT-5 measured |
| LMArena Vision: homework | 1278 | Not measured | Only GPT-5 measured |
| Kagi LLM Benchmark | 72.7% | 40.5% | GPT-5 |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| AIME | 93.4% | Not measured | Only GPT-5 measured |
| MATH 500 (Vals AI) | 96.0% | Not measured | Only GPT-5 measured |
| MGSM (Vals AI) | 92.8% | Not measured | Only GPT-5 measured |
| AA Math Index | 94.3 | Not measured | Only GPT-5 measured |
| AIME 2025 (Artificial Analysis run) | 94.3% | Not measured | Only GPT-5 measured |
| OTIS Mock AIME | 91.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tiers 1 to 3 | 55.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tier 4 | 22.0% | Not measured | Only GPT-5 measured |
| ProofBench | 18.0% | Not measured | Only GPT-5 measured |
| MATH level 5 | 98.1% | Not measured | Only GPT-5 measured |
| LMArena Text: math | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: mathematical work | 1405 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| MMLU Pro | 86.5% | Not measured | Only GPT-5 measured |
| LegalBench (Vals AI) | 86.0% | Not measured | Only GPT-5 measured |
| CaseLaw (Vals AI) | 66.5% | Not measured | Only GPT-5 measured |
| CorpFin (Vals AI) | 61.1% | Not measured | Only GPT-5 measured |
| TaxEval (Vals AI) | 73.4% | Not measured | Only GPT-5 measured |
| MortgageTax (Vals AI) | 65.5% | Not measured | Only GPT-5 measured |
| MedQA (Vals AI) | 96.3% | Not measured | Only GPT-5 measured |
| MedCode (Vals AI) | 49.6% | Not measured | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | 87.1% | Not measured | Only GPT-5 measured |
| SimpleQA Verified | 50.1% | Not measured | Only GPT-5 measured |
| GeoBench | 81.0% | Not measured | Only GPT-5 measured |
| LMArena Text: science | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: medicine and healthcare | 1413 | Not measured | Only GPT-5 measured |
| LMArena Text: legal and government | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: business and finance | 1400 | Not measured | Only GPT-5 measured |
| FACTS Benchmark Suite | 56.5% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1184 | Not measured | Only GPT-5 measured |
| Design Arena: UI components | 1180 | Not measured | Only GPT-5 measured |
| Design Arena: game dev | 1185 | Not measured | Only GPT-5 measured |
| Design Arena: data viz | 1221 | Not measured | Only GPT-5 measured |
| Design Arena: 3D | 1056 | Not measured | Only GPT-5 measured |
| Design Arena: all code categories | 1168 | Not measured | Only GPT-5 measured |
| Design Arena: SVG | 1189 | Not measured | Only GPT-5 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5 | Mistral Small 4 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.7% | Not measured | Only GPT-5 measured |
| MedScribe (Vals AI) | 83.7% | Not measured | Only GPT-5 measured |
| IFBench | 73.1% | 48.2% | GPT-5 |
| Long context reasoning (AA-LCR) | 78.2% | 49.7% | GPT-5 |
| DTBench | 90.7% | 70.9% | GPT-5 |
| LMCA | 40.0% | 20.6% | GPT-5 |
| Fiction.LiveBench | 97.2% | Not measured | Only GPT-5 measured |
| Creative writing (Lech Mazur) | 86.0% | Not measured | Only GPT-5 measured |
| LMArena Text | 1406 | Not measured | Only GPT-5 measured |
| LMArena Text: instruction following | 1388 | Not measured | Only GPT-5 measured |
| LMArena Text: creative writing | 1365 | Not measured | Only GPT-5 measured |
| LMArena Vision | 1232 | Not measured | Only GPT-5 measured |
| LMArena Text: English | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: non-English | 1397 | Not measured | Only GPT-5 measured |
| LMArena Text: Chinese | 1422 | Not measured | Only GPT-5 measured |
| LMArena Text: German | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: Japanese | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Korean | 1360 | Not measured | Only GPT-5 measured |
| LMArena Text: French | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Spanish | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: Russian | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: Polish | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: multi-turn | 1426 | Not measured | Only GPT-5 measured |
| LMArena Text: longer queries | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only GPT-5 measured |
| LMArena Text: entertainment and media | 1379 | Not measured | Only GPT-5 measured |
| LMArena Vision: English | 1231 | Not measured | Only GPT-5 measured |
| LMArena Vision: Chinese | 1267 | Not measured | Only GPT-5 measured |
| LMArena Vision: reading text | 1246 | Not measured | Only GPT-5 measured |
| LMArena Vision: humor | 1219 | Not measured | Only GPT-5 measured |
| LMArena Vision: captioning | 1212 | Not measured | Only GPT-5 measured |
| LMArena Vision: recognizing things | 1263 | Not measured | Only GPT-5 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 | Mistral Small 4 | 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 Small 4 costs less
Repository review
50K input + 3K output tokens
Mistral Small 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 4 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 | Mistral Small 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | March 16, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.15 |
| Output $/Mtok | $10.00 | $0.60 |
| Coding index | 38.8 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.39 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 better than Mistral Small 4 for coding?
Only GPT-5 has a coding score, 38.8 on the Vibe Coding Index; Mistral Small 4 has none, so the two cannot be ranked on coding. Pick Mistral Small 4 for output tokens 16.7x cheaper ($0.60 vs $10.00 per million).
Which is cheaper, GPT-5 or Mistral Small 4?
GPT-5 costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Small 4 costs $0.15 and $0.60. Cost per solved task is known for GPT-5 only.
Which has the bigger context window, GPT-5 or Mistral Small 4?
GPT-5, with 400K tokens against 262K.
How many benchmarks is the GPT-5 vs Mistral Small 4 comparison based on?
13 benchmarks have a result for both models as of October 21, 2026. GPT-5's coding index rests on 3 independent benchmarks and Mistral Small 4's on 1. Where only one model has a result, the page says so instead of naming a winner.