Model comparison
Claude Sonnet 4.6 vs Llama 3-8B
Claude Sonnet 4.6 is the stronger model overall, scoring 50.1 to 25.3 on the Noometry Index. Llama 3-8B costs 43× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. Claude Sonnet 4.6 scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Claude Sonnet 4.6 leads 51.6 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 85.8% for Claude Sonnet 4.6 and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.6.
- Claude Sonnet 4.6 accepts more context: 1M tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4.6 | Llama 3-8B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 50.1 | 25.3 |
| Released | 2026-02-17 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $3 | $0.14 |
| Output $ / M tokens | $15 | $0.14 |
| Results tracked | 57 | 34 |
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Category by category
Coding Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 46.0 (#62), Llama 3-8B: 30.5 (#285)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1504 | 1152 |
| SWE-bench Verified | 75.2% | — |
| DeepSWE | 29.9% | — |
| FrontierCode | 24.3% | — |
| LMArena WebDev | 1521 | — |
| SciCode | 46.8% | — |
| WeirdML | 66.1% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,327 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
Claude Sonnet 4.6: 39.1 (#26), Llama 3-8B: —
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| Terminal-Bench | 53.4% | — |
| APEX-Agents | 43% | — |
| OSWorld 2.0 | 9.3% | — |
| DeepResearch Bench | 54.9% | — |
| OSWorld | 72.1% | — |
| ExploitBench | 23.6% | — |
| GBAEval | 48.8% | — |
| GDP.pdf | 18% | — |
| LMArena Search | 1221 | — |
| Vending-Bench 2 | 7,204 | — |
Reasoning Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 46.1 (#40), Llama 3-8B: 13.8 (#321)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 13% | 0% |
| LMArena Hard Prompts | 1485 | 1133 |
| DTBench | 89.9% | 43.9% |
| Epoch Capabilities Index | 152.25 | 116.33 |
| ForecastBench | 62 | 58.6 |
| ARC-AGI-2 | 60.4% | — |
| NYT Connections (extended) | 80.9% | — |
| ARC-AGI-1 | 86.5% | — |
| CritPt | 3.1% | — |
| Thematic Generalization | 76.3% | — |
| Mystery Game Puzzles | 16% | — |
| LMCA | 46.5% | — |
| Adversarial NLI | — | 57.3% |
| WinoGrande | — | 75.7% |
Math Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 51.9 (#41), Llama 3-8B: 8.3 (#317)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 85.8% | 1.9% |
| LMArena Math | 1463 | 1151 |
| ProofBench | 45% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 8.3% | — |
Knowledge Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 51.6 (#60), Llama 3-8B: 7.8 (#303)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 87.4% | 26.1% |
| LMArena Expert | 1500 | 1113 |
| SimpleQA Verified | 35.5% | — |
| Vectara Hallucination Rate | 10.6% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
Claude Sonnet 4.6: 38.0 (#61), Llama 3-8B: —
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1283 | — |
| Blueprint-Bench 2 | 6.7% | — |
| LMArena Document | 1482 | — |
Multilingual Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 54.6 (#39), Llama 3-8B: 31.0 (#256)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1440 | 1098 |
| LMArena Chinese | 1491 | 1076 |
| LMArena French | 1465 | 1158 |
| LMArena German | 1428 | 1104 |
| LMArena Japanese | 1419 | 967 |
| LMArena Korean | 1411 | 1003 |
| LMArena Russian | 1440 | 1109 |
| LMArena Spanish | 1465 | 1172 |
Instruction Following Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 77.6 (#23), Llama 3-8B: 58.7 (#255)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1476 | 1127 |
Long Context Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 45.3 (#40), Llama 3-8B: 34.2 (#246)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1479 | 1128 |
Writing & Preference Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 70.4 (#22), Llama 3-8B: 37.7 (#251)
| Benchmark | Claude Sonnet 4.6 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1458 | 1166 |
| LMArena Creative Writing | 1435 | 1150 |
| LMArena Multi-Turn | 1464 | 1152 |
| EQ-Bench Creative Writing | 1810 | — |
| EQ-Bench 4 | 1207 | — |
Frequently asked questions
Is Claude Sonnet 4.6 better than Llama 3-8B?
Claude Sonnet 4.6 is the stronger model overall, scoring 50.1 to 25.3 on the Noometry Index. Llama 3-8B costs 43× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 4.6 or Llama 3-8B?
Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; Claude Sonnet 4.6 lists at $3 and $15.
Is Claude Sonnet 4.6 or Llama 3-8B better for coding?
Claude Sonnet 4.6 scores higher on coding benchmarks: 46.0 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 4.6 does, with 1M tokens against 8K.
How many benchmarks do Claude Sonnet 4.6 and Llama 3-8B share?
23 benchmarks have published results for both models. Claude Sonnet 4.6 has 57 scored results on Noometry and Llama 3-8B has 34.