GPT-4 vs Mistral Medium 3.5
Mistral Medium 3.5 leads GPT-4 on the Artificial Analysis Intelligence Index, 14.2 to 6.7, and Mistral Medium 3.5 costs 8.0x less per output token ($7.50 vs $60.00 per million). Updated October 22, 2026; 34 benchmarks have a result for both.
GPT-4 or Mistral Medium 3.5: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-4 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 34
- GPT-4 only
- 11
- Mistral Medium 3.5 only
- 47
- 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 Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Mistral Medium 3.5 has a GPQA Diamond result, 34.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.5 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.5
GPT-4 $0.060, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- GPT-4 not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Knowledge, 43.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Korean: GPT-4 1087 against Mistral Medium 3.5 1384, 49.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4 $60.00 against Mistral Medium 3.5 $7.50 per million output tokens, 8.0x apart.
- Context window
- GPT-4 takes 8K tokens, Mistral Medium 3.5 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-4 | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 19 | ||
| All shared boards | 0 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | 22.4% | Not measured | Only GPT-4 measured |
| SWE-bench Lite | 39.7% | Not measured | Only GPT-4 measured |
| SWE-bench (full test set) | 12.5% | Not measured | Only GPT-4 measured |
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | 13.1 | 46.9 | Mistral Medium 3.5 |
| SWE-bench Verified (Vals AI) | Not measured | 66.4% | Only Mistral Medium 3.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 measured |
| ALE-Bench | Not measured | 764.0 | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1209 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1195 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 14.2 | Mistral Medium 3.5 |
| Epoch Capabilities Index | 125.7 | 141.4 | Mistral Medium 3.5 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 34.9% | Only Mistral Medium 3.5 measured |
| GPQA Diamond (Artificial Analysis run) | 34.9% | 74.8% | Mistral Medium 3.5 |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 13.8% | Only Mistral Medium 3.5 measured |
| BIG-Bench Hard | 75.1% | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts | 1200 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1149 | 1432 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1209 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | Not measured | 41.4% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| GSM8K | 92.0% | Not measured | Only GPT-4 measured |
| LMArena Text: math | 1230 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1228 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.5 measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | Not measured | 58.8% | Only Mistral Medium 3.5 measured |
| TaxEval (Vals AI) | Not measured | 68.0% | Only Mistral Medium 3.5 measured |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 measured |
| MMLU Pro (Artificial Analysis run) | 56.2% | Not measured | Only GPT-4 measured |
| MMLU | 86.4% | Not measured | Only GPT-4 measured |
| AI2 Reasoning Challenge | 96.3% | Not measured | Only GPT-4 measured |
| LMArena Text: science | 1177 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1145 | 1409 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1191 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1145 | 1426 | Mistral Medium 3.5 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1142 | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | Not measured | 1076 | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | Not measured | 1120 | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | Not measured | 1060 | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | Not measured | 1121 | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | Not measured | 949 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1062 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | GPT-4 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | Not measured | 67.7% | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 33.2% | 68.8% | Mistral Medium 3.5 |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only Mistral Medium 3.5 measured |
| DTBench | 62.7% | 75.5% | Mistral Medium 3.5 |
| LMCA | 17.1% | 26.1% | Mistral Medium 3.5 |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| HellaSwag | 95.3% | Not measured | Only GPT-4 measured |
| WinoGrande | 87.5% | Not measured | Only GPT-4 measured |
| LMArena Text | 1206 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1201 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1191 | 1374 | Mistral Medium 3.5 |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1220 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1194 | 1404 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1184 | 1444 | Mistral Medium 3.5 |
| LMArena Text: German | 1198 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1137 | Not measured | Only GPT-4 measured |
| LMArena Text: Korean | 1087 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1220 | 1448 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1197 | 1411 | Mistral Medium 3.5 |
| LMArena Text: Russian | 1195 | 1395 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1265 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1206 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1190 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1219 | 1384 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1184 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 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-4 | Mistral Medium 3.5 | 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 3.5 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.5 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.5 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-4 | Mistral Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | April 30, 2026 |
| Context window | 8K | 262K |
| Input $/Mtok | $30.00 | $1.50 |
| Output $/Mtok | $60.00 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4 better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-4 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-4 or Mistral Medium 3.5?
GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, GPT-4 or Mistral Medium 3.5?
Mistral Medium 3.5, with 262K tokens against 8K.
How many benchmarks is the GPT-4 vs Mistral Medium 3.5 comparison based on?
34 benchmarks have a result for both models as of October 22, 2026. GPT-4's coding index rests on 0 independent benchmarks and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.