Model comparison
Claude Sonnet 5 vs Llama 3.1-8B
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 70× less per token, which makes it the better buy when Claude Sonnet 5's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. Claude Sonnet 5 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 math, where Claude Sonnet 5 leads 66.2 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.7% for Claude Sonnet 5 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 $2 / $10 for Claude Sonnet 5.
- Claude Sonnet 5 accepts more context: 1M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 5 | Llama 3.1-8B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 54.6 | 23.0 |
| Released | 2026-06-29 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $0.05 |
| Output $ / M tokens | $10 | $0.08 |
| Results tracked | 51 | 43 |
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Category by category
Coding Claude Sonnet 5 leads
Claude Sonnet 5: 55.5 (#26), Llama 3.1-8B: 20.2 (#340)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| SciCode | 54.3% | 13.2% |
| WeirdML | 68.8% | 1.7% |
| LMArena Coding | 1483 | 1195 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.7% | — |
| CursorBench | 34.1% | — |
| LMArena WebDev | 1541 | — |
| GSO | 37.3% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,463 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Claude Sonnet 5 leads
Claude Sonnet 5: 42.8 (#18), Llama 3.1-8B: 22.5 (#131)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 54.5% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
| GBAEval | 65.3% | — |
| LMArena Search | 1194 | — |
| Vending-Bench 2 | 6,378 | — |
Reasoning Claude Sonnet 5 leads
Claude Sonnet 5: 49.1 (#39), Llama 3.1-8B: 14.9 (#321)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| CritPt | 16.9% | 0% |
| Chess Puzzles | 35% | 0% |
| LMArena Hard Prompts | 1461 | 1175 |
| DTBench | 92.5% | 50.9% |
| LMCA | 50% | 5.4% |
| Epoch Capabilities Index | 156.21 | 116.57 |
| SimpleBench | 60.6% | — |
| NYT Connections (extended) | 75.1% | — |
| Mystery Game Puzzles | 35% | — |
| Surface Evolver Bench | 60% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 61.1 | — |
| PIQA | — | 81.2% |
Math Claude Sonnet 5 leads
Claude Sonnet 5: 66.2 (#27), Llama 3.1-8B: 10.2 (#317)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.7% | 1.7% |
| LMArena Math | 1467 | 1179 |
| FrontierMath (Tiers 1-3) | 65.6% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 77% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge Claude Sonnet 5 leads
Claude Sonnet 5: 55.6 (#47), Llama 3.1-8B: 8.0 (#307)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 90.5% | 27% |
| LMArena Expert | 1490 | 1144 |
| SimpleQA Verified | 33.7% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
Claude Sonnet 5: 42.4 (#31), Llama 3.1-8B: —
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1274 | — |
| Blueprint-Bench 2 | 24.9% | — |
| LMArena Document | 1466 | — |
Multilingual Claude Sonnet 5 leads
Claude Sonnet 5: 53.8 (#55), Llama 3.1-8B: 34.0 (#249)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1431 | 1148 |
| LMArena Chinese | 1477 | 1151 |
| LMArena French | 1460 | 1177 |
| LMArena German | 1440 | 1144 |
| LMArena Japanese | 1422 | 1061 |
| LMArena Korean | 1411 | 1053 |
| LMArena Russian | 1451 | 1158 |
| LMArena Spanish | 1437 | 1169 |
Instruction Following Claude Sonnet 5 leads
Claude Sonnet 5: 76.3 (#41), Llama 3.1-8B: 58.9 (#258)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1452 | 1159 |
| IFEval | — | 74.3% |
Long Context Claude Sonnet 5 leads
Claude Sonnet 5: 44.8 (#55), Llama 3.1-8B: 35.8 (#238)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1463 | 1182 |
Writing & Preference Claude Sonnet 5 leads
Claude Sonnet 5: 69.2 (#25), Llama 3.1-8B: 29.7 (#290)
| Benchmark | Claude Sonnet 5 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1442 | 1187 |
| LMArena Creative Writing | 1416 | 1154 |
| EQ-Bench Creative Writing | 1794 | 713 |
| LMArena Multi-Turn | 1454 | 1172 |
| WildBench | — | 68.7% |
| EQ-Bench 4 | 1236 | — |
Frequently asked questions
Is Claude Sonnet 5 better than Llama 3.1-8B?
Claude Sonnet 5 is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 70× less per token, which makes it the better buy when Claude Sonnet 5's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 5 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; Claude Sonnet 5 lists at $2 and $10.
Is Claude Sonnet 5 or Llama 3.1-8B better for coding?
Claude Sonnet 5 scores higher on coding benchmarks: 55.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 5 does, with 1M tokens against 128K.
How many benchmarks do Claude Sonnet 5 and Llama 3.1-8B share?
27 benchmarks have published results for both models. Claude Sonnet 5 has 51 scored results on Noometry and Llama 3.1-8B has 43.