GPT-4o-mini (2024-07-18) vs Mistral Medium 3
GPT-4o-mini (2024-07-18) costs 3.3x less per output token than Mistral Medium 3 ($0.60 vs $2.00 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
GPT-4o-mini (2024-07-18) or Mistral Medium 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini (2024-07-18) for output tokens 3.3x cheaper ($0.60 vs $2.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 31
- GPT-4o-mini (2024-07-18) only
- 15
- Mistral Medium 3 only
- 26
- Like-for-like categories
- 5 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
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
Mistral Medium 3
Mistral Medium 3 takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
GPT-4o-mini (2024-07-18) $0.0004, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- Reasoning, 16.7 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Vision: Chinese: GPT-4o-mini (2024-07-18) 1033 against Mistral Medium 3 1161, 21.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Mistral Medium 3 $2.00 per million output tokens, 3.3x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K 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-4o-mini (2024-07-18) | Bars | Mistral Medium 3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini (2024-07-18) | Mistral Medium 3 | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| 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 |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: coding | 1290 | 1385 | Mistral Medium 3 |
| LMArena Text: software and IT | 1291 | 1385 | Mistral Medium 3 |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 9.0 | Only Mistral Medium 3 measured |
| Epoch Capabilities Index | Not measured | 134.0 | Only Mistral Medium 3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 24.3% | Only Mistral Medium 3 measured |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini (2024-07-18) | 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 |
| GPQA Diamond (Epoch AI run) | 37.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| SimpleBench | 10.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Mystery game puzzles | 12.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | 1364 | Mistral Medium 3 |
| LMArena Text: expert | 1234 | 1342 | Mistral Medium 3 |
| LMArena Text: hard prompts in English | 1278 | 1374 | Mistral Medium 3 |
| 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 | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini (2024-07-18) | 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 |
| OTIS Mock AIME | 6.9% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| FrontierMath, tiers 1 to 3 | 0.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | 1353 | Mistral Medium 3 |
| LMArena Text: mathematical work | 1271 | 1354 | Mistral Medium 3 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o-mini (2024-07-18) | Mistral Medium 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 76.0% | Only Mistral Medium 3 measured |
| SimpleQA Verified | 8.3% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1286 | 1381 | Mistral Medium 3 |
| LMArena Text: medicine and healthcare | 1282 | 1381 | Mistral Medium 3 |
| LMArena Text: legal and government | 1305 | 1384 | Mistral Medium 3 |
| LMArena Text: business and finance | 1265 | 1352 | Mistral Medium 3 |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1079 | Only Mistral Medium 3 measured |
| Design Arena: UI components | Not measured | 1022 | Only Mistral Medium 3 measured |
| Design Arena: game dev | Not measured | 1017 | 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 | 1068 | 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 index020
| Benchmark | GPT-4o-mini (2024-07-18) | 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 | Not measured | 62.3% | Only Mistral Medium 3 measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | 1370 | Mistral Medium 3 |
| LMArena Text: instruction following | 1258 | 1338 | Mistral Medium 3 |
| LMArena Text: creative writing | 1268 | 1343 | Mistral Medium 3 |
| LMArena Vision | 1066 | 1157 | Mistral Medium 3 |
| LMArena Text: English | 1301 | 1385 | Mistral Medium 3 |
| LMArena Text: non-English | 1266 | 1353 | Mistral Medium 3 |
| LMArena Text: Chinese | 1265 | 1374 | Mistral Medium 3 |
| LMArena Text: German | 1272 | 1377 | Mistral Medium 3 |
| LMArena Text: Japanese | 1216 | 1316 | Mistral Medium 3 |
| LMArena Text: Korean | 1195 | 1304 | Mistral Medium 3 |
| LMArena Text: French | 1297 | 1368 | Mistral Medium 3 |
| LMArena Text: Spanish | 1276 | 1364 | Mistral Medium 3 |
| LMArena Text: Russian | 1275 | 1359 | Mistral Medium 3 |
| LMArena Text: Polish | 1244 | 1371 | Mistral Medium 3 |
| LMArena Text: multi-turn | 1285 | 1384 | Mistral Medium 3 |
| LMArena Text: longer queries | 1289 | 1360 | Mistral Medium 3 |
| LMArena Text: writing and language | 1282 | 1352 | Mistral Medium 3 |
| LMArena Text: entertainment and media | 1253 | 1335 | Mistral Medium 3 |
| LMArena Vision: English | 1072 | 1172 | Mistral Medium 3 |
| LMArena Vision: Chinese | 1033 | 1161 | Mistral Medium 3 |
| 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-4o-mini (2024-07-18) | 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
GPT-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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 (2024-07-18) | Mistral Medium 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | July 18, 2024 | May 7, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.15 | $0.40 |
| Output $/Mtok | $0.60 | $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-4o-mini (2024-07-18) better than Mistral Medium 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini (2024-07-18) for output tokens 3.3x cheaper ($0.60 vs $2.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-4o-mini (2024-07-18) or Mistral Medium 3?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 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-4o-mini (2024-07-18) or Mistral Medium 3?
Mistral Medium 3, with 131K tokens against 128K.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs Mistral Medium 3 comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini (2024-07-18)'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.