Model comparison
Llama 3.2 1B vs Mistral Large
Mistral Large is the stronger model overall, scoring 31.9 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Meta
Mistral AISummary
- They share 20 benchmarks with published results for both. Llama 3.2 1B scores higher in 1 category and Mistral Large in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Mistral Large leads 30.1 to 7.2.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 10.8% for Llama 3.2 1B and 38.4% for Mistral Large.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $2 / $6 for Mistral Large.
- Mistral Large accepts more context: 131K tokens versus 60K.
Side by side
| Llama 3.2 1B | Mistral Large | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 20.1 | 31.9 |
| Released | 2024-09-24 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 60K | 131K |
| Max output | 54K | 16K |
| Input $ / M tokens | $0.027 | $2 |
| Output $ / M tokens | $0.20 | $6 |
| Results tracked | 22 | 51 |
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Category by category
Coding Mistral Large leads
Llama 3.2 1B: 21.1 (#338), Mistral Large: 34.3 (#240)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| BigCodeBench Instruct | 8.2% | 30% |
| LMArena Coding | 1070 | 1277 |
| BigCodeBench Complete | 11.3% | 38.3% |
| SciCode | — | 36.2% |
| LiveBench Coding | — | 47.1% |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Mistral Large leads
Llama 3.2 1B: 14.6 (#150), Mistral Large: 28.6 (#89)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | 38.4% |
| BALROG | 6.6% | — |
Reasoning Too close to call
Llama 3.2 1B: 16.2 (#308), Mistral Large: 15.8 (#310)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1257 |
| Epoch Capabilities Index | 101.99 | 128.52 |
| SimpleBench | — | 22.5% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math Mistral Large leads
Llama 3.2 1B: 10.4 (#313), Mistral Large: 18.2 (#291)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 0.6% | 8.5% |
| LMArena Math | 1086 | 1262 |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Mistral Large leads
Llama 3.2 1B: 7.2 (#312), Mistral Large: 30.1 (#230)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| GPQA Diamond | 23.9% | 51.3% |
| LMArena Expert | 1007 | 1232 |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multilingual Mistral Large leads
Llama 3.2 1B: 23.8 (#292), Mistral Large: 40.0 (#219)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| LMArena Non-English | 973 | 1237 |
| LMArena Chinese | 959 | 1240 |
| LMArena German | 1014 | 1254 |
| LMArena Russian | 941 | 1257 |
| LMArena French | — | 1325 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
| LMArena Spanish | — | 1268 |
Instruction Following Mistral Large leads
Llama 3.2 1B: 52.4 (#290), Mistral Large: 67.9 (#191)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1031 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context Mistral Large leads
Llama 3.2 1B: 31.9 (#274), Mistral Large: 38.3 (#199)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1050 | 1261 |
Writing & Preference Mistral Large leads
Llama 3.2 1B: 21.3 (#310), Mistral Large: 40.7 (#242)
| Benchmark | Llama 3.2 1B | Mistral Large |
|---|---|---|
| LMArena Text | 1055 | 1266 |
| LMArena Creative Writing | 1033 | 1243 |
| EQ-Bench Creative Writing | 200 | 985 |
| LMArena Multi-Turn | 1030 | 1260 |
| Short-Story Creative Writing | — | 69% |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is Llama 3.2 1B better than Mistral Large?
Mistral Large is the stronger model overall, scoring 31.9 to 20.1 on the Noometry Index. Llama 3.2 1B costs 43× less per token, which makes it the better buy when Mistral Large's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Mistral Large?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Mistral Large lists at $2 and $6.
Is Llama 3.2 1B or Mistral Large better for coding?
Mistral Large scores higher on coding benchmarks: 34.3 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
Mistral Large does, with 131K tokens against 60K.
How many benchmarks do Llama 3.2 1B and Mistral Large share?
20 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Mistral Large has 51.