Model comparison
Claude Haiku 5.5 vs Llama 3.1-70B
Claude Haiku 5.5 is the stronger model overall, scoring 49.5 to 29.6 on the Noometry Index.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. Claude Haiku 5.5 scores higher in 4 categories and Llama 3.1-70B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Haiku 5.5 leads 73.6 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for Claude Haiku 5.5 and 3.6% for Llama 3.1-70B.
- Claude Haiku 5.5 is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- Claude Haiku 5.5 accepts more context: 1M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Claude Haiku 5.5 | Llama 3.1-70B | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 49.5 | 29.6 |
| Released | 2026-10-07 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.10 | $0.40 |
| Output $ / M tokens | $0.50 | $0.40 |
| Results tracked | 9 | 35 |
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Category by category
Coding Claude Haiku 5.5 leads
Claude Haiku 5.5: 49.2 (#50), Llama 3.1-70B: 30.3 (#296)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| LMArena WebDev | 1587 | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| LMArena Coding | — | 1260 |
| BigCodeBench Complete | — | 54.8% |
Agentic & Tool Use Not comparable
Claude Haiku 5.5: —, Llama 3.1-70B: 25.1 (#112)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
Reasoning Claude Haiku 5.5 leads
Claude Haiku 5.5: 35.2 (#69), Llama 3.1-70B: 21.6 (#220)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| NYT Connections (extended) | 65.7% | — |
| LMArena Hard Prompts | — | 1241 |
| Mystery Game Puzzles | 30% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
| Epoch Capabilities Index | — | 125.92 |
Math Claude Haiku 5.5 leads
Claude Haiku 5.5: 73.6 (#18), Llama 3.1-70B: 13.5 (#304)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 3.6% |
| FrontierMath (Tiers 1-3) | 75.1% | — |
| FrontierMath Tier 4 | 46.3% | — |
| Omni-MATH | — | 21% |
| LMArena Math | — | 1252 |
| MATH Level 5 | — | 36.7% |
Knowledge Claude Haiku 5.5 leads
Claude Haiku 5.5: 50.8 (#70), Llama 3.1-70B: 24.2 (#269)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 89.6% | 44.2% |
| SimpleQA Verified | 23.8% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| LMArena Expert | — | 1209 |
| MMLU | — | 80.1% |
Multimodal Not comparable
Claude Haiku 5.5: 43.7 (#21), Llama 3.1-70B: —
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| Furniture Assembly | 47.5% | — |
Multilingual Not comparable
Claude Haiku 5.5: —, Llama 3.1-70B: 38.8 (#225)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | — | 1219 |
| LMArena Chinese | — | 1215 |
| LMArena French | — | 1261 |
| LMArena German | — | 1222 |
| LMArena Japanese | — | 1132 |
| LMArena Korean | — | 1140 |
| LMArena Russian | — | 1234 |
| LMArena Spanish | — | 1253 |
Instruction Following Not comparable
Claude Haiku 5.5: —, Llama 3.1-70B: 65.3 (#223)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| IFEval | — | 82.1% |
| LMArena Instruction Following | — | 1231 |
Long Context Not comparable
Claude Haiku 5.5: —, Llama 3.1-70B: 37.6 (#214)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | — | 1241 |
Writing & Preference Not comparable
Claude Haiku 5.5: —, Llama 3.1-70B: 35.4 (#267)
| Benchmark | Claude Haiku 5.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | — | 1261 |
| LMArena Creative Writing | — | 1232 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |
| LMArena Multi-Turn | — | 1256 |
Frequently asked questions
Is Claude Haiku 5.5 better than Llama 3.1-70B?
Claude Haiku 5.5 is the stronger model overall, scoring 49.5 to 29.6 on the Noometry Index.
Which is cheaper, Claude Haiku 5.5 or Llama 3.1-70B?
Claude Haiku 5.5 is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is Claude Haiku 5.5 or Llama 3.1-70B better for coding?
Claude Haiku 5.5 scores higher on coding benchmarks: 49.2 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
Claude Haiku 5.5 does, with 1M tokens against 128K.
How many benchmarks do Claude Haiku 5.5 and Llama 3.1-70B share?
2 benchmarks have published results for both models. Claude Haiku 5.5 has 9 scored results on Noometry and Llama 3.1-70B has 35.