Model comparison
GPT-5.3 Chat vs Mistral Small
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Mistral Small costs 18× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Mistral AISummary
- They share 17 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Mistral Small in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.3 Chat leads 38.2 to 16.4.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Mistral Small accepts more context: 262K tokens versus 128K.
- Mistral Small has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Mistral Small | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 42.8 | 33.4 |
| Released | 2026-03-03 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 128K | 262K |
| Max output | 16K | 256K |
| Input $ / M tokens | $1.75 | $0.15 |
| Output $ / M tokens | $14 | $0.60 |
| Results tracked | 18 | 39 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.3 Chat leads
GPT-5.3 Chat: 41.4 (#124), Mistral Small: 34.0 (#247)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Coding | 1408 | 1362 |
| SciCode | — | 26.5% |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |
Agentic & Tool Use Not comparable
GPT-5.3 Chat: —, Mistral Small: 28.1 (#93)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.1% |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 28.5 (#102), Mistral Small: 19.8 (#250)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1335 |
| Kagi LLM Benchmark | — | 37.8% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| LiveBench | — | 44% |
Math GPT-5.3 Chat leads
GPT-5.3 Chat: 38.2 (#142), Mistral Small: 16.4 (#293)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Math | 1389 | 1341 |
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
Knowledge GPT-5.3 Chat leads
GPT-5.3 Chat: 38.8 (#140), Mistral Small: 31.0 (#222)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Expert | 1397 | 1291 |
| GPQA Diamond | — | 47.5% |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |
Multimodal Not comparable
GPT-5.3 Chat: —, Mistral Small: 33.5 (#96)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual GPT-5.3 Chat leads
GPT-5.3 Chat: 50.3 (#124), Mistral Small: 45.5 (#169)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Non-English | 1382 | 1315 |
| LMArena Chinese | 1432 | 1340 |
| LMArena French | 1397 | 1337 |
| LMArena German | 1384 | 1340 |
| LMArena Japanese | 1352 | 1275 |
| LMArena Korean | 1346 | 1259 |
| LMArena Russian | 1400 | 1324 |
| LMArena Spanish | 1371 | 1346 |
Instruction Following GPT-5.3 Chat leads
GPT-5.3 Chat: 72.8 (#129), Mistral Small: 66.4 (#209)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1378 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GPT-5.3 Chat leads
GPT-5.3 Chat: 42.6 (#120), Mistral Small: 40.4 (#156)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1396 | 1327 |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.1 (#68), Mistral Small: 52.5 (#171)
| Benchmark | GPT-5.3 Chat | Mistral Small |
|---|---|---|
| LMArena Text | 1389 | 1338 |
| LMArena Creative Writing | 1355 | 1305 |
| LMArena Multi-Turn | 1412 | 1344 |
| EQ-Bench Creative Writing | 1690 | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GPT-5.3 Chat better than Mistral Small?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 33.4 on the Noometry Index. Mistral Small costs 18× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Chat or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Mistral Small better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 128K.
How many benchmarks do GPT-5.3 Chat and Mistral Small share?
17 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Mistral Small has 39.