Model comparison
Llama-3.3-70B-Instruct vs Mistral Medium
Mistral Medium is the stronger model overall, scoring 36.0 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Mistral Medium's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Meta
Mistral AISummary
- They share 27 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 1 category and Mistral Medium in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where Mistral Medium leads 42.9 to 26.4.
- The biggest single-benchmark swing is MATH Level 5: 41.6% for Llama-3.3-70B-Instruct and 81.6% for Mistral Medium.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
- Mistral Medium accepts more context: 262K tokens versus 128K.
Side by side
| Llama-3.3-70B-Instruct | Mistral Medium | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 30.3 | 36.0 |
| Released | 2024-12-06 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 262K |
| Input $ / M tokens | $0.10 | $1.50 |
| Output $ / M tokens | $0.32 | $7.50 |
| Results tracked | 43 | 36 |
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Category by category
Coding Mistral Medium leads
Llama-3.3-70B-Instruct: 30.6 (#284), Mistral Medium: 33.8 (#241)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| SciCode | 26% | 40.2% |
| WeirdML | 14.4% | 43.7% |
| LMArena Coding | 1268 | 1434 |
| FrontierCode | — | 8% |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 763.98 |
Agentic & Tool Use Mistral Medium leads
Llama-3.3-70B-Instruct: 25.6 (#100), Mistral Medium: 28.1 (#84)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | 37.7% |
| BALROG | 23% | — |
Reasoning Mistral Medium leads
Llama-3.3-70B-Instruct: 13.7 (#325), Mistral Medium: 23.3 (#165)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1257 | 1426 |
| DTBench | 59.5% | 75.5% |
| LMCA | 17.5% | 26.1% |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 50% |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| Surface Evolver Bench | — | 26.9% |
| Epoch Capabilities Index | 127.32 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Mistral Medium leads
Llama-3.3-70B-Instruct: 14.3 (#292), Mistral Medium: 27.2 (#238)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 32.2% |
| LMArena Math | 1268 | 1409 |
| MATH Level 5 | 41.6% | 81.6% |
| ProofBench | — | 9% |
| LiveBench Math | 42.2% | — |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Llama-3.3-70B-Instruct leads
Llama-3.3-70B-Instruct: 30.5 (#220), Mistral Medium: 24.9 (#260)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| GPQA Diamond | 47.4% | 59.5% |
| Vectara Hallucination Rate | 4.1% | 22.7% |
| LMArena Expert | 1225 | 1408 |
| Humanity's Last Exam | — | 4.5% |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, Mistral Medium: 34.6 (#82)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| LMArena Vision | — | 1172 |
Multilingual Mistral Medium leads
Llama-3.3-70B-Instruct: 40.1 (#215), Mistral Medium: 52.3 (#86)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| LMArena Non-English | 1236 | 1408 |
| LMArena Chinese | 1217 | 1448 |
| LMArena French | 1280 | 1460 |
| LMArena German | 1251 | 1433 |
| LMArena Japanese | 1149 | 1378 |
| LMArena Korean | 1142 | 1382 |
| LMArena Russian | 1252 | 1411 |
| LMArena Spanish | 1269 | 1433 |
Instruction Following Mistral Medium leads
Llama-3.3-70B-Instruct: 71.3 (#152), Mistral Medium: 73.9 (#111)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | 1242 | 1398 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Mistral Medium leads
Llama-3.3-70B-Instruct: 26.4 (#290), Mistral Medium: 42.9 (#108)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| LMArena Longer Query | 1256 | 1406 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Mistral Medium leads
Llama-3.3-70B-Instruct: 47.8 (#203), Mistral Medium: 60.2 (#97)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium |
|---|---|---|
| LMArena Text | 1274 | 1424 |
| LMArena Creative Writing | 1250 | 1391 |
| LMArena Multi-Turn | 1279 | 1418 |
| Short-Story Creative Writing | — | 77.3% |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Mistral Medium?
Mistral Medium is the stronger model overall, scoring 36.0 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 19× less per token, which makes it the better buy when Mistral Medium's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Mistral Medium?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Mistral Medium lists at $1.50 and $7.50.
Is Llama-3.3-70B-Instruct or Mistral Medium better for coding?
Mistral Medium scores higher on coding benchmarks: 33.8 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Mistral Medium does, with 262K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Mistral Medium share?
27 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mistral Medium has 36.