GPT-5.5 vs Mistral Large
GPT-5.5 leads Mistral Large on the Artificial Analysis Intelligence Index, 38.4 to 5.8, and Mistral Large costs 5.0x less per output token ($6.00 vs $30.00 per million). Updated October 21, 2026; 37 benchmarks have a result for both.
GPT-5.5 or Mistral Large: which to pick
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.
Pick Mistral Large for output tokens 5.0x cheaper ($6.00 vs $30.00 per million).
- Shared results
- 37
- GPT-5.5 only
- 97
- Mistral Large only
- 7
- Like-for-like categories
- 6 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-5.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 5.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 47.7%.
Same board, both measured
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 69.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.5 has a Design Arena: websites result, 1256.
Directional only
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large
GPT-5.5 $0.020, Mistral Large $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only
At a glance
- Coding index
- GPT-5.5 71.3 from 5 benchmarks, Mistral Large not benchmarked.
- Widest category
- Reasoning, 45.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-5.5 93.5% against Mistral Large 35.1%, 58.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 $30.00 against Mistral Large $6.00 per million output tokens, 5.0x apart.
- Context window
- GPT-5.5 takes 1.05M tokens, Mistral Large 128K.
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-5.5 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 37 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | Not measured | Only GPT-5.5 measured |
| AA Coding Index | 74.9 | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | 85.3% | 37.1% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.6% | Not measured | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | 69.8% | Not measured | Only GPT-5.5 measured |
| Code migration (Vals AI) | 45.2% | Not measured | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | Not measured | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | 62.5% | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 17.8% | Only Mistral Large measured |
| SciCode | 56.1% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | Not measured | Only GPT-5.5 measured |
| GSO-Bench | 40.2% | Not measured | Only GPT-5.5 measured |
| WeirdML | 84.9% | Not measured | Only GPT-5.5 measured |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | Not measured | Only GPT-5.5 measured |
| LMArena Text: coding | 1494 | 1275 | GPT-5.5 |
| LMArena Text: software and IT | 1493 | 1274 | GPT-5.5 |
| LMArena WebDev: React | 1506 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: HTML | 1543 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: from an image | 1532 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 5.8 | GPT-5.5 |
| Epoch Capabilities Index | 159.7 | 122.1 | GPT-5.5 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building web apps | 1117 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | 1089 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | 1143 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | 1163 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1173 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building games | 1179 | Not measured | Only GPT-5.5 measured |
| Design Arena: HTML slides | 1067 | Not measured | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | 1109 | Not measured | Only GPT-5.5 measured |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | Not measured | Only GPT-5.5 measured |
| APEX-Agents | 55.1% | Not measured | Only GPT-5.5 measured |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | Not measured | Only GPT-5.5 measured |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| Vending-Bench 2 | $7,524 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | 47.7% | GPT-5.5 |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 93.5% | 35.1% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | 3.5% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 90.7% | Not measured | Only GPT-5.5 measured |
| ARC-AGI | 95.0% | Not measured | Only GPT-5.5 measured |
| ARC-AGI-2 | 85.0% | Not measured | Only GPT-5.5 measured |
| SimpleBench | 69.0% | Not measured | Only GPT-5.5 measured |
| Chess puzzles | 63.0% | Not measured | Only GPT-5.5 measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-5.5 measured |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1489 | 1257 | GPT-5.5 |
| LMArena Text: expert | 1507 | 1208 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1483 | 1268 | GPT-5.5 |
| LMArena Vision: diagrams | 1325 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: homework | 1332 | Not measured | Only GPT-5.5 measured |
| Kagi LLM Benchmark | 88.8% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| OTIS Mock AIME | 84.4% | Not measured | Only GPT-5.5 measured |
| FrontierMath, tiers 1 to 3 | 85.3% | Not measured | Only GPT-5.5 measured |
| FrontierMath, tier 4 | 72.5% | Not measured | Only GPT-5.5 measured |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: math | 1487 | 1261 | GPT-5.5 |
| LMArena Text: mathematical work | 1496 | 1257 | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | 69.7% | GPT-5.5 |
| LegalBench (Vals AI) | 86.5% | Not measured | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | 66.2% | Not measured | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 68.4% | Not measured | Only GPT-5.5 measured |
| TaxEval (Vals AI) | 75.0% | 63.8% | GPT-5.5 |
| MortgageTax (Vals AI) | 68.8% | Not measured | Only GPT-5.5 measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MedCode (Vals AI) | 49.1% | Not measured | Only GPT-5.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 51.5% | Only Mistral Large measured |
| SimpleQA Verified | 63.0% | Not measured | Only GPT-5.5 measured |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1485 | 1260 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1465 | 1255 | GPT-5.5 |
| LMArena Text: legal and government | 1491 | 1275 | GPT-5.5 |
| LMArena Text: business and finance | 1478 | 1238 | GPT-5.5 |
| FACTS Benchmark Suite | 66.7% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | Not measured | Only GPT-5.5 measured |
| Design Arena: UI components | 1260 | Not measured | Only GPT-5.5 measured |
| Design Arena: game dev | 1294 | Not measured | Only GPT-5.5 measured |
| Design Arena: data viz | 1265 | Not measured | Only GPT-5.5 measured |
| Design Arena: 3D | 1198 | Not measured | Only GPT-5.5 measured |
| Design Arena: all code categories | 1260 | Not measured | Only GPT-5.5 measured |
| Design Arena: SVG | 1234 | Not measured | Only GPT-5.5 measured |
| Design Arena: ASCII art | 1255 | Not measured | Only GPT-5.5 measured |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GPT-5.5 | Mistral Large | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | Not measured | Only GPT-5.5 measured |
| MedScribe (Vals AI) | 86.9% | Not measured | Only GPT-5.5 measured |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | Not measured | Only GPT-5.5 measured |
| Long context reasoning (AA-LCR) | 84.3% | Not measured | Only GPT-5.5 measured |
| DTBench | 96.0% | 60.8% | GPT-5.5 |
| LMCA | 54.3% | Not measured | Only GPT-5.5 measured |
| Surface Evolver Bench | 88.1% | Not measured | Only GPT-5.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | 1265 | GPT-5.5 |
| LMArena Text: instruction following | 1478 | 1249 | GPT-5.5 |
| LMArena Text: creative writing | 1455 | 1242 | GPT-5.5 |
| LMArena Vision | 1297 | Not measured | Only GPT-5.5 measured |
| LMArena Text: English | 1470 | 1284 | GPT-5.5 |
| LMArena Text: non-English | 1467 | 1235 | GPT-5.5 |
| LMArena Text: Chinese | 1534 | 1239 | GPT-5.5 |
| LMArena Text: German | 1480 | 1242 | GPT-5.5 |
| LMArena Text: Japanese | 1499 | 1170 | GPT-5.5 |
| LMArena Text: Korean | 1460 | 1202 | GPT-5.5 |
| LMArena Text: French | 1485 | 1325 | GPT-5.5 |
| LMArena Text: Spanish | 1468 | 1268 | GPT-5.5 |
| LMArena Text: Russian | 1473 | 1252 | GPT-5.5 |
| LMArena Text: Polish | 1465 | 1157 | GPT-5.5 |
| LMArena Text: multi-turn | 1476 | 1260 | GPT-5.5 |
| LMArena Text: longer queries | 1484 | 1261 | GPT-5.5 |
| LMArena Text: writing and language | 1466 | 1261 | GPT-5.5 |
| LMArena Text: entertainment and media | 1449 | 1230 | GPT-5.5 |
| LMArena Vision: English | 1292 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: Chinese | 1360 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: reading text | 1306 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: humor | 1293 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | 1231 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-5.5 measured |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 | Mistral Large | 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 Large costs less
Repository review
50K input + 3K output tokens
Mistral Large costs less
Agent loop
200K input + 10K output tokens
Mistral Large 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-5.5 | Mistral Large |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | February 26, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $5.00 | $2.00 |
| Output $/Mtok | $30.00 | $6.00 |
| Coding index | 71.3 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $1.65 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than Mistral Large for coding?
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Pick Mistral Large for output tokens 5.0x cheaper ($6.00 vs $30.00 per million).
Which is cheaper, GPT-5.5 or Mistral Large?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for GPT-5.5 only.
Which has the bigger context window, GPT-5.5 or Mistral Large?
GPT-5.5, with 1.05M tokens against 128K.
How many benchmarks is the GPT-5.5 vs Mistral Large comparison based on?
37 benchmarks have a result for both models as of October 21, 2026. GPT-5.5's coding index rests on 5 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.