Model comparison
Llama-3.3-70B-Instruct vs Mistral Medium 3.5
Mistral Medium 3.5 is the stronger model overall, scoring 39.6 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 3.5's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Meta
Mistral AISummary
- They share 17 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Mistral Medium 3.5 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Mistral Medium 3.5 leads 36.4 to 14.3.
- 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 3.5.
- Mistral Medium 3.5 accepts more context: 262K tokens versus 128K.
Side by side
| Llama-3.3-70B-Instruct | Mistral Medium 3.5 | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 30.3 | 39.6 |
| Released | 2024-12-06 | — |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 4K | 210K |
| Input $ / M tokens | $0.10 | $1.50 |
| Output $ / M tokens | $0.32 | $7.50 |
| Results tracked | 43 | 22 |
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Category by category
Coding Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 30.6 (#284), Mistral Medium 3.5: 35.7 (#208)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Coding | 1268 | 1461 |
| LMArena WebDev | — | 1263 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), Mistral Medium 3.5: —
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 13.7 (#325), Mistral Medium 3.5: 16.6 (#290)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1436 |
| Epoch Capabilities Index | 127.32 | 141.42 |
| SimpleBench | 19.9% | — |
| Kagi LLM Benchmark | — | 41.4% |
| NYT Connections (extended) | — | 12.9% |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 14.3 (#292), Mistral Medium 3.5: 36.4 (#113)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Math | 1268 | 1432 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
Knowledge Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 30.5 (#220), Mistral Medium 3.5: 39.3 (#123)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Expert | 1225 | 1431 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, Mistral Medium 3.5: 37.6 (#64)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Vision | — | 1222 |
Multilingual Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 40.1 (#215), Mistral Medium 3.5: 52.1 (#93)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Non-English | 1236 | 1405 |
| LMArena Chinese | 1217 | 1443 |
| LMArena French | 1280 | 1448 |
| LMArena German | 1251 | 1453 |
| LMArena Korean | 1142 | 1387 |
| LMArena Russian | 1252 | 1396 |
| LMArena Spanish | 1269 | 1406 |
| LMArena Japanese | 1149 | — |
Instruction Following Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 71.3 (#152), Mistral Medium 3.5: 74.7 (#87)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1414 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 26.4 (#290), Mistral Medium 3.5: 43.2 (#101)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Longer Query | 1256 | 1414 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Mistral Medium 3.5 leads
Llama-3.3-70B-Instruct: 47.8 (#203), Mistral Medium 3.5: 58.6 (#113)
| Benchmark | Llama-3.3-70B-Instruct | Mistral Medium 3.5 |
|---|---|---|
| LMArena Text | 1274 | 1421 |
| LMArena Creative Writing | 1250 | 1372 |
| LMArena Multi-Turn | 1279 | 1423 |
| EQ-Bench 4 | — | 993 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Mistral Medium 3.5?
Mistral Medium 3.5 is the stronger model overall, scoring 39.6 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 3.5's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Mistral Medium 3.5?
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 3.5 lists at $1.50 and $7.50.
Is Llama-3.3-70B-Instruct or Mistral Medium 3.5 better for coding?
Mistral Medium 3.5 scores higher on coding benchmarks: 35.7 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Mistral Medium 3.5 does, with 262K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Mistral Medium 3.5 share?
17 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mistral Medium 3.5 has 22.