Llama 3.1 Cerebras Instruct on MMMU: score, rank and what it means
Llama 3.1 Cerebras Instruct scores 71.3 on MMMU, placing it #264 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.
Score distribution
The chart shows the top 10 models on MMMU, with Llama 3.1 Cerebras Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 4 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 5 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 6 | Grok 4 Pro | xAI | 1000000 | $19.00 | 85.4 |
| 7 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 8 | Claude Sonnet 4.5 Pro | Anthropic | 1000000 | $18.73 | 86.7 |
| 9 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 10 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
What this score means
On MMMU, Llama 3.1 Cerebras Instruct sits at #264 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 15.6 points. The median model in this index scores 70.0, which puts Llama 3.1 Cerebras Instruct above the midpoint.
Cerebras models average 62.9 on this benchmark. Llama 3.1 Cerebras Instruct is 8.4 points above that provider line, so it is a stronger result than the average Cerebras model.
Among multimodal models, the average is 74.3. Llama 3.1 Cerebras Instruct is 3.0 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 71.3 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $4.11 per million input tokens, Llama 3.1 Cerebras Instruct is above the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Sonar Reasoning | Perplexity | 200000 | $11.48 | 76.2 |
| Firefunction Max | Fireworks AI | 32768 | $9.12 | 73.2 |
| Fusion Pro | OpenRouter | 65536 | $8.69 | 72.2 |
| Gemini 2.5 Pro Mini | Google DeepMind | 128000 | $10.07 | 74.2 |
| Llama 3.1 Cerebras Instruct | Cerebras | 65536 | $4.11 | 76.1 |
| Llama 3.1 Turbo Instruct | Together AI | 32768 | $4.21 | 76.7 |
| Llama 3 Lambda Base | Lambda | 8192 | $3.19 | 72.9 |
Other benchmarks for Llama 3.1 Cerebras Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 78.3 | 232 | 61th | |
| GPQA Diamond | 72.1 | 218 | 64th | |
| SWE-bench Verified | 65.9 | 196 | 67th | |
| HumanEval | 81.3 | 205 | 66th | |
| MATH-500 | 80.9 | 193 | 68th | |
| AIME 2025 | 69.2 | 276 | 54th | |
| LiveCodeBench | 67.1 | 246 | 59th | |
| IFEval | 85.8 | 165 | 73th | |
| TruthfulQA | 77.2 | 274 | 54th | |
| HellaSwag | 87.7 | 154 | 74th | |
| Arena Elo | 1388.4 | 256 | 57th |
FAQ
What score does Llama 3.1 Cerebras Instruct get on MMMU?
Llama 3.1 Cerebras Instruct scores 71.3 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.
How does Llama 3.1 Cerebras Instruct rank on MMMU?
It ranks #264 of 600 models, in the 56th percentile. The leader is GPT-5.2 Max at 86.9.
Is Llama 3.1 Cerebras Instruct good at MMMU?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the MMMU score calculated?
MMMU is a multimodal benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Llama 3.1 Cerebras Instruct cost?
Llama 3.1 Cerebras Instruct costs $4.11 per million input tokens and $20.72 per million output tokens.