Model comparison
Llama 3.2 1B vs Mixtral 8x7B
Mixtral 8x7B is the stronger model overall, scoring 26.8 to 19.9 on the Noometry Index. Llama 3.2 1B costs 9.9× less per token, which makes it the better buy when Mixtral 8x7B's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Meta
Mistral AISummary
- They share 15 benchmarks with published results for both. Llama 3.2 1B scores higher in 1 category and Mixtral 8x7B in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Mixtral 8x7B leads 34.4 to 21.4.
- The biggest single-benchmark swing is GPQA Diamond: 23.9% for Llama 3.2 1B and 30.6% for Mixtral 8x7B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- Llama 3.2 1B accepts more context: 60K tokens versus 32K.
Side by side
| Llama 3.2 1B | Mixtral 8x7B | |
|---|---|---|
| Provider | Meta | Mistral AI |
| Noometry Index | 19.9 | 26.8 |
| Released | 2024-09-24 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 60K | 32K |
| Max output | 54K | 32K |
| Input $ / M tokens | $0.027 | $0.70 |
| Output $ / M tokens | $0.20 | $0.70 |
| Results tracked | 22 | 38 |
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Category by category
Coding Mixtral 8x7B leads
Llama 3.2 1B: 20.6 (#333), Mixtral 8x7B: 32.2 (#265)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1071 | 1127 |
| BigCodeBench Instruct | 8.2% | — |
| BigCodeBench Complete | 11.3% | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
Llama 3.2 1B: 14.5 (#142), Mixtral 8x7B: —
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 10.8% | — |
| BALROG | 6.6% | — |
Reasoning Mixtral 8x7B leads
Llama 3.2 1B: 15.6 (#304), Mixtral 8x7B: 17.4 (#283)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1044 | 1115 |
| Epoch Capabilities Index | 101.95 | 118.38 |
| Chess Puzzles | 0% | — |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math Mixtral 8x7B leads
Llama 3.2 1B: 9.7 (#308), Mixtral 8x7B: 17.2 (#284)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1086 | 1147 |
| OTIS Mock AIME 2024-2025 | 0.6% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge Mixtral 8x7B leads
Llama 3.2 1B: 7.2 (#307), Mixtral 8x7B: 11.0 (#296)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 23.9% | 30.6% |
| LMArena Expert | 1007 | 1088 |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual Mixtral 8x7B leads
Llama 3.2 1B: 23.9 (#287), Mixtral 8x7B: 29.8 (#261)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 973 | 1077 |
| LMArena Chinese | 958 | 1055 |
| LMArena German | 1014 | 1114 |
| LMArena Russian | 941 | 1090 |
| LMArena French | — | 1165 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Spanish | — | 1110 |
Instruction Following Llama 3.2 1B leads
Llama 3.2 1B: 52.6 (#285), Mixtral 8x7B: 51.2 (#292)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1031 | 1110 |
| IFEval | — | 57.5% |
Long Context Mixtral 8x7B leads
Llama 3.2 1B: 31.9 (#269), Mixtral 8x7B: 33.4 (#255)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1050 | 1103 |
Writing & Preference Mixtral 8x7B leads
Llama 3.2 1B: 21.4 (#305), Mixtral 8x7B: 34.4 (#265)
| Benchmark | Llama 3.2 1B | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1055 | 1132 |
| LMArena Creative Writing | 1033 | 1109 |
| LMArena Multi-Turn | 1030 | 1115 |
| EQ-Bench Creative Writing | 200 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is Llama 3.2 1B better than Mixtral 8x7B?
Mixtral 8x7B is the stronger model overall, scoring 26.8 to 19.9 on the Noometry Index. Llama 3.2 1B costs 9.9× less per token, which makes it the better buy when Mixtral 8x7B's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 1B or Mixtral 8x7B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.
Is Llama 3.2 1B or Mixtral 8x7B better for coding?
Mixtral 8x7B scores higher on coding benchmarks: 32.2 versus 20.6 in the Noometry coding category.
Which has the bigger context window?
Llama 3.2 1B does, with 60K tokens against 32K.
How many benchmarks do Llama 3.2 1B and Mixtral 8x7B share?
15 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and Mixtral 8x7B has 38.