Model comparison
GPT-5 Mini vs Llama 3.1-8B
GPT-5 Mini is the stronger model overall, scoring 41.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
MetaSummary
- They share 34 benchmarks with published results for both. GPT-5 Mini scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Mini leads 45.6 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.7% for GPT-5 Mini and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 41.8 | 23.0 |
| Released | 2025-08-07 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.25 | $0.05 |
| Output $ / M tokens | $2 | $0.08 |
| Results tracked | 60 | 43 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.1 (#146), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| SciCode | 39.2% | 13.2% |
| WeirdML | 52.7% | 1.7% |
| LMArena Coding | 1406 | 1195 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| SWE-bench Multilingual | 39.7% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 799.77 | — |
| AlgoTune | 1.38 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GPT-5 Mini leads
GPT-5 Mini: 31.1 (#70), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 55.5% | 25.8% |
| Terminal-Bench | 34.8% | — |
| BALROG | — | 15.1% |
| Vending-Bench 2 | -31.18 | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 23.9 (#168), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| CritPt | 0% | 0% |
| Chess Puzzles | 30% | 0% |
| LMArena Hard Prompts | 1380 | 1175 |
| DTBench | 80.5% | 50.9% |
| LMCA | 34.2% | 5.4% |
| Epoch Capabilities Index | 145.52 | 116.57 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 54.3% | — |
| EnigmaEval | 8.2% | — |
| Mystery Game Puzzles | 10% | — |
| ForecastBench | 61 | — |
| PIQA | — | 81.2% |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.7% | 1.7% |
| Omni-MATH | 72.2% | 13.7% |
| LMArena Math | 1378 | 1179 |
| MATH Level 5 | 97.8% | 22.9% |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| ProofBench | 9% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 75% | 27% |
| MMLU-Pro | 83.5% | 40.6% |
| GPQA (HELM) | 75.6% | 24.7% |
| LMArena Expert | 1379 | 1144 |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| Confabulations | 13.3% | — |
| Vectara Hallucination Rate | 12.9% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-5 Mini: 35.6 (#85), Llama 3.1-8B: —
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual GPT-5 Mini leads
GPT-5 Mini: 48.9 (#137), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1363 | 1148 |
| LMArena Chinese | 1385 | 1151 |
| LMArena French | 1386 | 1177 |
| LMArena German | 1366 | 1144 |
| LMArena Japanese | 1341 | 1061 |
| LMArena Korean | 1308 | 1053 |
| LMArena Russian | 1362 | 1158 |
| LMArena Spanish | 1355 | 1169 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| IFEval | 92.7% | 74.3% |
| LMArena Instruction Following | 1357 | 1159 |
Long Context GPT-5 Mini leads
GPT-5 Mini: 41.9 (#132), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1355 | 1182 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference GPT-5 Mini leads
GPT-5 Mini: 55.2 (#148), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-5 Mini | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1373 | 1187 |
| LMArena Creative Writing | 1325 | 1154 |
| EQ-Bench Creative Writing | 1313 | 713 |
| WildBench | 85.5% | 68.7% |
| LMArena Multi-Turn | 1363 | 1172 |
| Short-Story Creative Writing | 83.1% | — |
Frequently asked questions
Is GPT-5 Mini better than Llama 3.1-8B?
GPT-5 Mini is the stronger model overall, scoring 41.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 12× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Which is cheaper, GPT-5 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-5 Mini lists at $0.25 and $2.
Is GPT-5 Mini or Llama 3.1-8B better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 128K.
How many benchmarks do GPT-5 Mini and Llama 3.1-8B share?
34 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Llama 3.1-8B has 43.