GPT-5.5 Pro vs Mistral Medium 3
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
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-5.5 Pro has the edge where the context window is larger, 1.05M against 131K tokens.
- Shared results
- 2
- GPT-5.5 Pro only
- 7
- Mistral Medium 3 only
- 55
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Medium 3 has a AA Intelligence Index result, 9.0.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3 has a Design Arena: websites result, 1079.
Directional only
Long documents
Listed context window
GPT-5.5 Pro
GPT-5.5 Pro takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3
GPT-5.5 Pro $0.120, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-5.5 Pro not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- Intelligence, 36.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: GPT-5.5 Pro 162.9 against Mistral Medium 3 134.0, 36.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 Pro $180.00 against Mistral Medium 3 $2.00 per million output tokens, 90.0x apart.
- Context window
- GPT-5.5 Pro takes 1.05M 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-5.5 Pro | Bars | Mistral Medium 3 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.0% | Only Mistral Medium 3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Mistral Medium 3 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 |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 9.0 | Only Mistral Medium 3 measured |
| Epoch Capabilities Index | 162.9 | 134.0 | GPT-5.5 Pro |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 24.3% | Only Mistral Medium 3 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 57.8% | Only Mistral Medium 3 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.1% | Only Mistral Medium 3 measured |
| ARC-AGI | 96.5% | Not measured | Only GPT-5.5 Pro measured |
| ARC-AGI-2 | 84.6% | Not measured | Only GPT-5.5 Pro measured |
| SimpleBench | 76.9% | Not measured | Only GPT-5.5 Pro measured |
| Chess puzzles | 62.0% | Not measured | Only GPT-5.5 Pro measured |
| 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 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 30.3 | Only Mistral Medium 3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 30.3% | Only Mistral Medium 3 measured |
| FrontierMath, tiers 1 to 3 | 87.7% | Not measured | Only GPT-5.5 Pro measured |
| FrontierMath, tier 4 | 78.0% | Not measured | Only GPT-5.5 Pro measured |
| 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 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 76.0% | Only Mistral Medium 3 measured |
| 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 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1079 | Only Mistral Medium 3 measured |
| Design Arena: UI components | Not measured | 1024 | Only Mistral Medium 3 measured |
| Design Arena: game dev | Not measured | 1018 | Only Mistral Medium 3 measured |
| Design Arena: data viz | Not measured | 1034 | Only Mistral Medium 3 measured |
| Design Arena: 3D | Not measured | 1083 | Only Mistral Medium 3 measured |
| Design Arena: all code categories | Not measured | 1069 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.5 Pro | Mistral Medium 3 | Leader |
|---|---|---|---|
| IFBench | Not measured | 39.3% | Only Mistral Medium 3 measured |
| Long context reasoning (AA-LCR) | Not measured | 31.3% | Only Mistral Medium 3 measured |
| DTBench | 96.0% | 62.3% | GPT-5.5 Pro |
| LMCA | 53.9% | Not measured | Only GPT-5.5 Pro 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 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 Pro | 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
Mistral Medium 3 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3 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 Pro | Mistral Medium 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | May 7, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $30.00 | $0.40 |
| Output $/Mtok | $180.00 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 Pro better than Mistral Medium 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-5.5 Pro has the edge where the context window is larger, 1.05M against 131K tokens.
Which is cheaper, GPT-5.5 Pro or Mistral Medium 3?
GPT-5.5 Pro costs $30.00 per million input tokens and $180.00 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-5.5 Pro or Mistral Medium 3?
GPT-5.5 Pro, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.5 Pro vs Mistral Medium 3 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-5.5 Pro's coding index rests on 0 independent benchmarks and Mistral Medium 3's on 0. Where only one model has a result, the page says so instead of naming a winner.