GPT-5.4 vs Mistral Nemo
Updated October 21, 2026. 2 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-5.4 has a coding score, 64.4 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding.
Mistral Nemo has the edge where output tokens cost 100.0x less.
- Shared results
- 2
- GPT-5.4 only
- 107
- Mistral Nemo only
- 1
- Like-for-like categories
- 2 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-5.4 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.4 has a AA Intelligence Index result, 39.0.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.4 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.4 has a AIME result, 96.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.4 has a MMLU Pro result, 87.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.4 has a Design Arena: websites result, 1199.
Directional only
Long documents
Listed context window
GPT-5.4
GPT-5.4 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
GPT-5.4 $0.010, Mistral Nemo $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.4 has the number, $1.02.
Directional only
At a glance
- Coding index
- GPT-5.4 64.4 from 3 benchmarks, Mistral Nemo not benchmarked.
- Widest category
- Intelligence, 48.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: GPT-5.4 157.3 against Mistral Nemo 118.7, 48.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.4 $15.00 against Mistral Nemo $0.15 per million output tokens, 100.0x apart.
- Context window
- GPT-5.4 takes 1.05M tokens, Mistral Nemo 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-5.4 | Bars | Mistral Nemo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index19 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| LMArena WebDev | 1464 | Not measured | Only GPT-5.4 measured |
| AA Coding Index | 71.1 | Not measured | Only GPT-5.4 measured |
| LiveCodeBench (Vals AI) | 84.1% | Not measured | Only GPT-5.4 measured |
| SWE-bench Verified (Vals AI) | 78.2% | Not measured | Only GPT-5.4 measured |
| Vibe Code Bench (Vals AI) | 67.4% | Not measured | Only GPT-5.4 measured |
| Code migration (Vals AI) | 35.0% | Not measured | Only GPT-5.4 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.4 measured |
| SkillsBench (Vals AI) | 51.7% | Not measured | Only GPT-5.4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 57.6% | Not measured | Only GPT-5.4 measured |
| Terminal-Bench | 81.8% | Not measured | Only GPT-5.4 measured |
| GSO-Bench | 31.4% | Not measured | Only GPT-5.4 measured |
| WeirdML | 77.7% | Not measured | Only GPT-5.4 measured |
| MirrorCode | 15.6% | Not measured | Only GPT-5.4 measured |
| ALE-Bench | 1607.0 | Not measured | Only GPT-5.4 measured |
| LMArena Text: coding | 1496 | Not measured | Only GPT-5.4 measured |
| LMArena Text: software and IT | 1491 | Not measured | Only GPT-5.4 measured |
| LMArena WebDev: React | 1454 | Not measured | Only GPT-5.4 measured |
| LMArena WebDev: HTML | 1478 | Not measured | Only GPT-5.4 measured |
| LMArena WebDev: from an image | 1436 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.0 | Not measured | Only GPT-5.4 measured |
| Epoch Capabilities Index | 157.3 | 118.7 | GPT-5.4 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1059 | Not measured | Only GPT-5.4 measured |
| Design Arena: agents building full-stack apps | 1017 | Not measured | Only GPT-5.4 measured |
| Design Arena: agents building mobile apps | 1087 | Not measured | Only GPT-5.4 measured |
| Design Arena: agents building Android apps | 1030 | Not measured | Only GPT-5.4 measured |
| Design Arena: agents building Godot games | 1135 | Not measured | Only GPT-5.4 measured |
| τ²-Bench Telecom | 87.1% | Not measured | Only GPT-5.4 measured |
| APEX-Agents | 52.4% | Not measured | Only GPT-5.4 measured |
| METR time horizons | 74.3% | Not measured | Only GPT-5.4 measured |
| DeepResearch Bench | 35.1% | Not measured | Only GPT-5.4 measured |
| PostTrainBench | 19.0% | Not measured | Only GPT-5.4 measured |
| Vending-Bench 2 | $6,144 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.4 measured |
| MMMU Pro (Vals AI) | 87.5% | Not measured | Only GPT-5.4 measured |
| GPQA Diamond (Artificial Analysis run) | 92.0% | Not measured | Only GPT-5.4 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.7% | Not measured | Only GPT-5.4 measured |
| GPQA Diamond (Epoch AI run) | 93.3% | Not measured | Only GPT-5.4 measured |
| ARC-AGI | 93.7% | Not measured | Only GPT-5.4 measured |
| ARC-AGI-2 | 74.0% | Not measured | Only GPT-5.4 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.4 measured |
| Mystery game puzzles | 37.0% | Not measured | Only GPT-5.4 measured |
| EBR-bench | 25.4% | Not measured | Only GPT-5.4 measured |
| LMArena Text: hard prompts | 1485 | Not measured | Only GPT-5.4 measured |
| LMArena Text: expert | 1507 | Not measured | Only GPT-5.4 measured |
| LMArena Text: hard prompts in English | 1479 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: diagrams | 1327 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: homework | 1336 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| AIME | 96.7% | Not measured | Only GPT-5.4 measured |
| OTIS Mock AIME | 97.8% | Not measured | Only GPT-5.4 measured |
| FrontierMath, tiers 1 to 3 | 78.6% | Not measured | Only GPT-5.4 measured |
| FrontierMath, tier 4 | 49.0% | Not measured | Only GPT-5.4 measured |
| ProofBench | 56.0% | Not measured | Only GPT-5.4 measured |
| LMArena Text: math | 1488 | Not measured | Only GPT-5.4 measured |
| LMArena Text: mathematical work | 1497 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| MMLU Pro | 87.5% | Not measured | Only GPT-5.4 measured |
| LegalBench (Vals AI) | 86.0% | Not measured | Only GPT-5.4 measured |
| CaseLaw (Vals AI) | 63.8% | Not measured | Only GPT-5.4 measured |
| CorpFin (Vals AI) | 65.3% | Not measured | Only GPT-5.4 measured |
| TaxEval (Vals AI) | 74.0% | Not measured | Only GPT-5.4 measured |
| MortgageTax (Vals AI) | 68.3% | Not measured | Only GPT-5.4 measured |
| MedQA (Vals AI) | 96.1% | Not measured | Only GPT-5.4 measured |
| MedCode (Vals AI) | 41.3% | Not measured | Only GPT-5.4 measured |
| SimpleQA Verified | 45.1% | Not measured | Only GPT-5.4 measured |
| MMLU | Not measured | 65.3% | Only Mistral Nemo measured |
| LMArena Text: science | 1479 | Not measured | Only GPT-5.4 measured |
| LMArena Text: medicine and healthcare | 1458 | Not measured | Only GPT-5.4 measured |
| LMArena Text: legal and government | 1486 | Not measured | Only GPT-5.4 measured |
| LMArena Text: business and finance | 1474 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: websites | 1199 | Not measured | Only GPT-5.4 measured |
| Design Arena: UI components | 1215 | Not measured | Only GPT-5.4 measured |
| Design Arena: game dev | 1195 | Not measured | Only GPT-5.4 measured |
| Design Arena: data viz | 1196 | Not measured | Only GPT-5.4 measured |
| Design Arena: 3D | 1066 | Not measured | Only GPT-5.4 measured |
| Design Arena: all code categories | 1180 | Not measured | Only GPT-5.4 measured |
| Design Arena: SVG | 1142 | Not measured | Only GPT-5.4 measured |
| Design Arena: ASCII art | 1188 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.4 | Mistral Nemo | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.3% | Not measured | Only GPT-5.4 measured |
| MedScribe (Vals AI) | 77.5% | Not measured | Only GPT-5.4 measured |
| IFBench | 73.9% | Not measured | Only GPT-5.4 measured |
| Long context reasoning (AA-LCR) | 82.0% | Not measured | Only GPT-5.4 measured |
| DTBench | 94.4% | 48.6% | GPT-5.4 |
| LMCA | 52.0% | Not measured | Only GPT-5.4 measured |
| CL-bench | 27.9% | Not measured | Only GPT-5.4 measured |
| Furniture assembly | 37.5% | Not measured | Only GPT-5.4 measured |
| CL-bench Life | 21.7% | Not measured | Only GPT-5.4 measured |
| LMArena Text | 1469 | Not measured | Only GPT-5.4 measured |
| LMArena Text: instruction following | 1469 | Not measured | Only GPT-5.4 measured |
| LMArena Text: creative writing | 1439 | Not measured | Only GPT-5.4 measured |
| LMArena Vision | 1303 | Not measured | Only GPT-5.4 measured |
| LMArena Text: English | 1467 | Not measured | Only GPT-5.4 measured |
| LMArena Text: non-English | 1465 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Chinese | 1520 | Not measured | Only GPT-5.4 measured |
| LMArena Text: German | 1472 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Japanese | 1485 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Korean | 1449 | Not measured | Only GPT-5.4 measured |
| LMArena Text: French | 1493 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Spanish | 1452 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Russian | 1480 | Not measured | Only GPT-5.4 measured |
| LMArena Text: Polish | 1472 | Not measured | Only GPT-5.4 measured |
| LMArena Text: multi-turn | 1482 | Not measured | Only GPT-5.4 measured |
| LMArena Text: longer queries | 1473 | Not measured | Only GPT-5.4 measured |
| LMArena Text: writing and language | 1458 | Not measured | Only GPT-5.4 measured |
| LMArena Text: entertainment and media | 1438 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: English | 1301 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: Chinese | 1354 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: reading text | 1312 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: humor | 1284 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: recognizing things | 1251 | Not measured | Only GPT-5.4 measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.4 measured |
| LMArena Document | 1471 | Not measured | Only GPT-5.4 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.4 | Mistral Nemo | 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 Nemo costs less
Repository review
50K input + 3K output tokens
Mistral Nemo costs less
Agent loop
200K input + 10K output tokens
Mistral Nemo 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-5.4 | Mistral Nemo |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | March 5, 2026 | July 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $2.50 | $0.15 |
| Output $/Mtok | $15.00 | $0.15 |
| Coding index | 64.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $1.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.4 better than Mistral Nemo for coding?
Only GPT-5.4 has a coding score, 64.4 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding. Mistral Nemo has the edge where output tokens cost 100.0x less.
Which is cheaper, GPT-5.4 or Mistral Nemo?
GPT-5.4 costs $2.50 per million input tokens and $15.00 per million output tokens; Mistral Nemo costs $0.15 and $0.15. Cost per solved task is known for GPT-5.4 only.
Which has the bigger context window, GPT-5.4 or Mistral Nemo?
GPT-5.4, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.4 vs Mistral Nemo comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-5.4's coding index rests on 3 independent benchmarks and Mistral Nemo's on 0. Where only one model has a result, the page says so instead of naming a winner.