GPT-4 vs Mistral Large 2407
Updated October 21, 2026. 6 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-4 has the edge where what matters is AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 6
- GPT-4 only
- 39
- Mistral Large 2407 only
- 11
- Like-for-like categories
- 4 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
Mistral Large 2407
Mistral Large 2407 leads, 7.6 against 6.7.
Same board, both measured
Long documents
Listed context window
Mistral Large 2407
Mistral Large 2407 takes 131K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 2407
GPT-4 $0.060, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Reasoning, 12.3 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Mistral Large 2407 47.2%, 12.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4 $60.00 against Mistral Large 2407 $6.00 per million output tokens, 10.0x apart.
- Context window
- GPT-4 takes 8K tokens, Mistral Large 2407 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-4 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 1 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | GPT-4 | Mistral Large 2407 | 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 (Artificial Analysis run) | Not measured | 26.7% | Only Mistral Large 2407 measured |
| LMArena Text: coding | 1209 | Not measured | Only GPT-4 measured |
| LMArena Text: software and IT | 1195 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 7.6 | Mistral Large 2407 |
| Epoch Capabilities Index | 125.7 | Not measured | Only GPT-4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 33.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 34.9% | 47.2% | Mistral Large 2407 |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.9% | Only Mistral Large 2407 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| BIG-Bench Hard | 75.1% | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts | 1200 | Not measured | Only GPT-4 measured |
| LMArena Text: expert | 1149 | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts in English | 1209 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Mistral Large 2407 measured |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| GSM8K | 92.0% | Not measured | Only GPT-4 measured |
| LMArena Text: math | 1230 | Not measured | Only GPT-4 measured |
| LMArena Text: mathematical work | 1228 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 56.2% | 68.3% | Mistral Large 2407 |
| MMLU | 86.4% | 80.0% | GPT-4 |
| AI2 Reasoning Challenge | 96.3% | Not measured | Only GPT-4 measured |
| LMArena Text: science | 1177 | Not measured | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | 1145 | Not measured | Only GPT-4 measured |
| LMArena Text: legal and government | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: business and finance | 1145 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 33.2% | 31.6% | GPT-4 |
| Long context reasoning (AA-LCR) | Not measured | 2.0% | Only Mistral Large 2407 measured |
| DTBench | 62.7% | 61.2% | GPT-4 |
| LMCA | 17.1% | Not measured | Only GPT-4 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| HellaSwag | 95.3% | Not measured | Only GPT-4 measured |
| WinoGrande | 87.5% | Not measured | Only GPT-4 measured |
| LMArena Text | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: instruction following | 1201 | Not measured | Only GPT-4 measured |
| LMArena Text: creative writing | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: English | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: non-English | 1194 | Not measured | Only GPT-4 measured |
| LMArena Text: Chinese | 1184 | Not measured | Only GPT-4 measured |
| LMArena Text: German | 1198 | Not measured | Only GPT-4 measured |
| LMArena Text: Japanese | 1137 | Not measured | Only GPT-4 measured |
| LMArena Text: Korean | 1087 | Not measured | Only GPT-4 measured |
| LMArena Text: French | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: Spanish | 1197 | Not measured | Only GPT-4 measured |
| LMArena Text: Russian | 1195 | Not measured | Only GPT-4 measured |
| LMArena Text: Polish | 1265 | Not measured | Only GPT-4 measured |
| LMArena Text: multi-turn | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: longer queries | 1190 | Not measured | Only GPT-4 measured |
| LMArena Text: writing and language | 1219 | Not measured | Only GPT-4 measured |
| LMArena Text: entertainment and media | 1184 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Mistral Large 2407 | 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 2407 costs less
Repository review
50K input + 3K output tokens
Mistral Large 2407 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 2407 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 2407 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | November 19, 2024 |
| Context window | 8K | 131K |
| 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 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-4 has the edge where what matters is AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GPT-4 or Mistral Large 2407?
GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Mistral Large 2407 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 2407?
Mistral Large 2407, with 131K tokens against 8K.
How many benchmarks is the GPT-4 vs Mistral Large 2407 comparison based on?
6 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 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.