Anthropic Claude Sonnet 4.6 vs Meta Code Llama 70B — side-by-side specs.
Anthropic Claude Sonnet 4.6 | Meta Code Llama 70B | |
|---|---|---|
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Feb 17 2026 | Jan 29 2024 |
| Model type | — | — |
| Open source | No | Yes |
| Specifications | ||
Parameters | — | — |
Context window | — | — |
| Benchmarks | ||
Coding SWE-Bench VerifiedReal coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 79.6% | — |
Multi-step tool use MCP AtlasCan the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 69.5% | — |
Multidisciplinary reasoning Humanity's Last Exam · no toolsHumanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 33.2% | — |
Abstract reasoning ARC-AGI-2Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 58.3% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 89.9% | — |
Agentic computer use OSWorld-VerifiedCan the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 72.5% | — |
Agentic financial analysis Finance Agent v2Tests the AI on real financial-analysis work, like digging through reports and making sound decisions. Higher is better. | 51% | — |
Knowledge work GDPval-AAMeasures how well the AI does economically valuable knowledge work, judged against human experts. Shown as a rating (like a chess Elo) — higher is better. | 1676 | — |
Chart reasoning CharXiv ReasoningCan the AI read and reason about complex charts and figures, not just text? Higher is better. | 72.4% | — |
Multimodal reasoning MMMU-ProA tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 74.5% | — |
Spatial reasoning Blueprint-Bench 2Can the AI reason about space and layout — for example, understanding a floor plan or blueprint? Higher is better. | 6.7% | — |
Long context MRCR v2 (8-needle) · 128k averageTests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 84.9% | — |
| Timeline | ||
| Release gap | Code Llama 70B shipped 750 days before Claude Sonnet 4.6 | |
Claude Sonnet 4.6 and Code Llama 70B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 750 days before Claude Sonnet 4.6, so benchmark comparisons should account for the intervening progress.
Code Llama 70B is an open-source / open-weight model; Claude Sonnet 4.6 is proprietary.
Direct benchmark comparisons are unavailable — Claude Sonnet 4.6 and Code Llama 70B don't publish scores on any of the same benchmarks.