Model comparison
GPT-4.1 mini vs Llama 3.1-8B
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
MetaSummary
- They share 35 benchmarks with published results for both. GPT-4.1 mini scores higher in 7 categories and Llama 3.1-8B in 2 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-4.1 mini leads 34.7 to 8.0.
- The biggest single-benchmark swing is MATH Level 5: 87.3% for GPT-4.1 mini and 22.9% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 mini | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 33.6 | 23.0 |
| Released | 2025-04-14 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 33K | 4K |
| Input $ / M tokens | $0.40 | $0.05 |
| Output $ / M tokens | $1.60 | $0.08 |
| Results tracked | 47 | 43 |
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Category by category
Coding GPT-4.1 mini leads
GPT-4.1 mini: 30.6 (#293), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| SciCode | 40.4% | 13.2% |
| WeirdML | 37.6% | 1.7% |
| BigCodeBench Instruct | 48.9% | 32.8% |
| LMArena Coding | 1367 | 1195 |
| SWE-bench Verified (bash only) | 23.9% | — |
| Aider Polyglot | 32.4% | — |
| BigCodeBench Complete | — | 40.5% |
| CadEval | 16% | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GPT-4.1 mini leads
GPT-4.1 mini: 33.3 (#55), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | 25.8% |
| BALROG | — | 15.1% |
Reasoning Llama 3.1-8B leads
GPT-4.1 mini: 10.8 (#340), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| CritPt | 0% | 0% |
| Chess Puzzles | 7% | 0% |
| LMArena Hard Prompts | 1349 | 1175 |
| DTBench | 68.8% | 50.9% |
| LMCA | 21.1% | 5.4% |
| Epoch Capabilities Index | 135.01 | 116.57 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 48.6% | — |
| ARC-AGI-1 | 3.5% | — |
| Mystery Game Puzzles | 7% | — |
| PIQA | — | 81.2% |
Math GPT-4.1 mini leads
GPT-4.1 mini: 24.1 (#270), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 44.7% | 1.7% |
| Omni-MATH | 49.1% | 13.7% |
| LMArena Math | 1343 | 1179 |
| MATH Level 5 | 87.3% | 22.9% |
| FrontierMath (Tiers 1-3) | 6.7% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-4.1 mini leads
GPT-4.1 mini: 34.7 (#194), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 65.8% | 27% |
| MMLU-Pro | 78.3% | 40.6% |
| GPQA (HELM) | 61.4% | 24.7% |
| LMArena Expert | 1338 | 1144 |
| SimpleQA Verified | 12.7% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-4.1 mini: 35.8 (#82), Llama 3.1-8B: —
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1181 | — |
Multilingual GPT-4.1 mini leads
GPT-4.1 mini: 45.7 (#166), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1318 | 1148 |
| LMArena Chinese | 1329 | 1151 |
| LMArena French | 1358 | 1177 |
| LMArena German | 1351 | 1144 |
| LMArena Japanese | 1290 | 1061 |
| LMArena Korean | 1298 | 1053 |
| LMArena Russian | 1324 | 1158 |
| LMArena Spanish | 1319 | 1169 |
Instruction Following GPT-4.1 mini leads
GPT-4.1 mini: 73.7 (#118), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| IFEval | 90.4% | 74.3% |
| LMArena Instruction Following | 1333 | 1159 |
Long Context Llama 3.1-8B leads
GPT-4.1 mini: 31.8 (#275), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1344 | 1182 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference GPT-4.1 mini leads
GPT-4.1 mini: 48.6 (#199), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-4.1 mini | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1340 | 1187 |
| LMArena Creative Writing | 1300 | 1154 |
| EQ-Bench Creative Writing | 1147 | 713 |
| WildBench | 83.8% | 68.7% |
| LMArena Multi-Turn | 1354 | 1172 |
Frequently asked questions
Is GPT-4.1 mini better than Llama 3.1-8B?
GPT-4.1 mini is the stronger model overall, scoring 33.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when GPT-4.1 mini's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 mini or Llama 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-4.1 mini lists at $0.40 and $1.60.
Is GPT-4.1 mini or Llama 3.1-8B better for coding?
GPT-4.1 mini scores higher on coding benchmarks: 30.6 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 128K.
How many benchmarks do GPT-4.1 mini and Llama 3.1-8B share?
35 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Llama 3.1-8B has 43.