GPT-4o-mini vs Mistral Large
Updated October 21, 2026. 16 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-4o-mini has the edge where output tokens cost 10.0x less, and where what matters is LiveCodeBench (Vals AI) or GPQA Diamond, where it leads.
- Shared results
- 16
- GPT-4o-mini only
- 11
- Mistral Large only
- 28
- Like-for-like categories
- 6 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
GPT-4o-mini
GPT-4o-mini leads, 6.7 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
Mistral Large
Mistral Large leads, 47.7% against 44.2%.
Same board, both measured
Math
AIME
GPT-4o-mini
GPT-4o-mini leads, 11.5% against 9.2%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large
Mistral Large leads, 69.7% against 62.7%.
Same board, both measured
Long documents
Listed context window
No call
Both take 128K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini
GPT-4o-mini $0.0004, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini not benchmarked, Mistral Large not benchmarked.
- Widest category
- Coding, 8.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): GPT-4o-mini 64.8% against Mistral Large 51.5%, 13.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against Mistral Large $6.00 per million output tokens, 10.0x apart.
- Context window
- Both take 128K tokens.
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-4o-mini | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 2 | 1 | ||
| Math | 1 | 2 | ||
| Knowledge | 2 | 3 | ||
| General | 0 | 1 | ||
| All shared boards | 8 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | 26.4% | 37.1% | Mistral Large |
| LiveCodeBench (Artificial Analysis run) | 23.4% | 17.8% | GPT-4o-mini |
| LMArena Text: coding | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: software and IT | Not measured | 1274 | Only Mistral Large measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 5.8 | GPT-4o-mini |
| Epoch Capabilities Index | 126.5 | 122.1 | GPT-4o-mini |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | 47.7% | Mistral Large |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | 35.1% | GPT-4o-mini |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 3.5% | GPT-4o-mini |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: hard prompts | Not measured | 1257 | Only Mistral Large measured |
| LMArena Text: expert | Not measured | 1208 | Only Mistral Large measured |
| LMArena Text: hard prompts in English | Not measured | 1268 | Only Mistral Large measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| AIME | 11.5% | 9.2% | GPT-4o-mini |
| MATH 500 (Vals AI) | 72.6% | 74.4% | Mistral Large |
| MGSM (Vals AI) | 86.2% | 87.2% | Mistral Large |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: math | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: mathematical work | Not measured | 1257 | Only Mistral Large measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | 69.7% | Mistral Large |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | 63.8% | Mistral Large |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | 76.2% | Mistral Large |
| MMLU Pro (Artificial Analysis run) | 64.8% | 51.5% | GPT-4o-mini |
| MMLU | 81.8% | 68.8% | GPT-4o-mini |
| LMArena Text: science | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: medicine and healthcare | Not measured | 1255 | Only Mistral Large measured |
| LMArena Text: legal and government | Not measured | 1275 | Only Mistral Large measured |
| LMArena Text: business and finance | Not measured | 1238 | Only Mistral Large measured |
| Neither model has a result on the other 6 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | Mistral Large | Leader |
|---|---|---|---|
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| DTBench | 54.4% | 60.8% | Mistral Large |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| LMArena Text | Not measured | 1265 | Only Mistral Large measured |
| LMArena Text: instruction following | Not measured | 1249 | Only Mistral Large measured |
| LMArena Text: creative writing | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: English | Not measured | 1284 | Only Mistral Large measured |
| LMArena Text: non-English | Not measured | 1235 | Only Mistral Large measured |
| LMArena Text: Chinese | Not measured | 1239 | Only Mistral Large measured |
| LMArena Text: German | Not measured | 1242 | Only Mistral Large measured |
| LMArena Text: Japanese | Not measured | 1170 | Only Mistral Large measured |
| LMArena Text: Korean | Not measured | 1202 | Only Mistral Large measured |
| LMArena Text: French | Not measured | 1325 | Only Mistral Large measured |
| LMArena Text: Spanish | Not measured | 1268 | Only Mistral Large measured |
| LMArena Text: Russian | Not measured | 1252 | Only Mistral Large measured |
| LMArena Text: Polish | Not measured | 1157 | Only Mistral Large measured |
| LMArena Text: multi-turn | Not measured | 1260 | Only Mistral Large measured |
| LMArena Text: longer queries | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: writing and language | Not measured | 1261 | Only Mistral Large measured |
| LMArena Text: entertainment and media | Not measured | 1230 | Only Mistral Large measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | 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
GPT-4o-mini costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini 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-4o-mini | Mistral Large |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | July 18, 2024 | February 26, 2024 |
| Context window | 128K | 128K |
| Input $/Mtok | $0.15 | $2.00 |
| Output $/Mtok | $0.60 | $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-4o-mini better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-4o-mini has the edge where output tokens cost 10.0x less, and where what matters is LiveCodeBench (Vals AI) or GPQA Diamond, where it leads.
Which is cheaper, GPT-4o-mini or Mistral Large?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 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-4o-mini or Mistral Large?
Neither: both take 128K tokens.
How many benchmarks is the GPT-4o-mini vs Mistral Large comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini'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.