GPT-4 vs Mistral Large
GPT-4 leads Mistral Large on the Artificial Analysis Intelligence Index, 6.7 to 5.8, and Mistral Large costs 10.0x less per output token ($6.00 vs $60.00 per million). Updated October 21, 2026; 34 benchmarks have a result for both.
GPT-4 or Mistral Large: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 34
- GPT-4 only
- 11
- Mistral Large only
- 10
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-4
GPT-4 leads, 6.7 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Mistral Large
Mistral Large takes 128K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large
GPT-4 $0.060, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4 not benchmarked, Mistral Large not benchmarked.
- Widest category
- Knowledge, 14.0 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena Text: Korean: GPT-4 1087 against Mistral Large 1202, 19.1 points apart on a 0 to 100 scale.
- Output price
- GPT-4 $60.00 against Mistral Large $6.00 per million output tokens, 10.0x apart.
- Context window
- GPT-4 takes 8K 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-4 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 4 | ||
| General | 2 | 16 | ||
| All shared boards | 6 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4 | Mistral Large | 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 |
| AA Coding Index | 13.1 | Not measured | Only GPT-4 measured |
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 17.8% | Only Mistral Large measured |
| LMArena Text: coding | 1209 | 1275 | Mistral Large |
| LMArena Text: software and IT | 1195 | 1274 | Mistral Large |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 5.8 | GPT-4 |
| Epoch Capabilities Index | 125.7 | 122.1 | GPT-4 |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 34.9% | 35.1% | Mistral Large |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.5% | Only Mistral Large measured |
| BIG-Bench Hard | 75.1% | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts | 1200 | 1257 | Mistral Large |
| LMArena Text: expert | 1149 | 1208 | Mistral Large |
| LMArena Text: hard prompts in English | 1209 | 1268 | Mistral Large |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 | 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 |
| GSM8K | 92.0% | Not measured | Only GPT-4 measured |
| LMArena Text: math | 1230 | 1261 | Mistral Large |
| LMArena Text: mathematical work | 1228 | 1257 | Mistral Large |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 56.2% | 51.5% | GPT-4 |
| MMLU | 86.4% | 68.8% | GPT-4 |
| AI2 Reasoning Challenge | 96.3% | Not measured | Only GPT-4 measured |
| LMArena Text: science | 1177 | 1260 | Mistral Large |
| LMArena Text: medicine and healthcare | 1145 | 1255 | Mistral Large |
| LMArena Text: legal and government | 1191 | 1275 | Mistral Large |
| LMArena Text: business and finance | 1145 | 1238 | Mistral Large |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index216
| Benchmark | GPT-4 | Mistral Large | Leader |
|---|---|---|---|
| IFBench | 33.2% | Not measured | Only GPT-4 measured |
| DTBench | 62.7% | 60.8% | GPT-4 |
| LMCA | 17.1% | Not measured | Only GPT-4 measured |
| HellaSwag | 95.3% | Not measured | Only GPT-4 measured |
| WinoGrande | 87.5% | Not measured | Only GPT-4 measured |
| LMArena Text | 1206 | 1265 | Mistral Large |
| LMArena Text: instruction following | 1201 | 1249 | Mistral Large |
| LMArena Text: creative writing | 1191 | 1242 | Mistral Large |
| LMArena Text: English | 1220 | 1284 | Mistral Large |
| LMArena Text: non-English | 1194 | 1235 | Mistral Large |
| LMArena Text: Chinese | 1184 | 1239 | Mistral Large |
| LMArena Text: German | 1198 | 1242 | Mistral Large |
| LMArena Text: Japanese | 1137 | 1170 | Mistral Large |
| LMArena Text: Korean | 1087 | 1202 | Mistral Large |
| LMArena Text: French | 1220 | 1325 | Mistral Large |
| LMArena Text: Spanish | 1197 | 1268 | Mistral Large |
| LMArena Text: Russian | 1195 | 1252 | Mistral Large |
| LMArena Text: Polish | 1265 | 1157 | GPT-4 |
| LMArena Text: multi-turn | 1206 | 1260 | Mistral Large |
| LMArena Text: longer queries | 1190 | 1261 | Mistral Large |
| LMArena Text: writing and language | 1219 | 1261 | Mistral Large |
| LMArena Text: entertainment and media | 1184 | 1230 | Mistral Large |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | 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-4 | Mistral Large |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | February 26, 2024 |
| Context window | 8K | 128K |
| Input $/Mtok | $30.00 | $2.00 |
| Output $/Mtok | $60.00 | $6.00 |
| 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-4 better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-4 or Mistral Large?
GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4 or Mistral Large?
Mistral Large, with 128K tokens against 8K.
How many benchmarks is the GPT-4 vs Mistral Large comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. GPT-4's coding index rests on 0 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.