Claude Opus 4.7 benchmarks: the key scores and what to trust

The benchmark numbers at a glance

Read that table conservatively. It is a snapshot of public claims, not a substitute for testing the model on real workloads.

Why SWE-bench Verified carries the most weight

The 87.6% SWE-bench Verified score is the best-supported Claude Opus 4.7 benchmark in the material reviewed. A migration and benchmark guide and LLM-Stats both cite the same result.

LLM-Stats also describes the score as a 6.8 percentage point improvement over Opus 4.6. ALM Corp, meanwhile, frames Opus 4.7 as stronger on hard coding and agentic workflows.

For software teams, that makes SWE-bench Verified the most practical public starting point. It still does not answer the question that matters in production: how the model behaves in your repositories, with your tests, tools, review standards and latency constraints.

GPQA: an impressive number, but verify before you bet on it

The 94.2% GPQA figure is clearly reported by LLM-Stats. The official Anthropic page is still important as primary material, but the available excerpt for this check mainly confirms that developers can use claude-opus-4-7 through the Claude API; it does not expose a fully citable benchmark table in the information reviewed here.

That does not make the GPQA number irrelevant. It does mean teams should treat it as a useful secondary signal rather than the strongest basis for procurement, migration, or model-routing decisions. If GPQA performance is central to your use case, cross-check the primary material and run your own evaluation.

SWE-bench Multilingual: promising for global codebases, still lightly sourced

The cited 80.5% SWE-bench Multilingual result is notable for teams working across languages and non-English development contexts. One public item reports the score and compares it with 77.8% for Opus 4.6.

The caveat is source depth. This figure does not appear as broadly in the available material as SWE-bench Verified. Treat it as a lead worth investigating, not as a final verdict.

What the benchmarks leave out

Claude Opus 4.7 is not being positioned only through leaderboard scores. VentureBeat describes Anthropic as publicly releasing its most powerful large language model yet. ALM Corp describes Opus 4.7 as a generally available Opus model aimed at demanding coding, agentic, document-heavy and vision workflows.

For real deployments, several product details may matter as much as the headline scores:

• Context window: LLM-Stats lists a 1 million-token context.
• Vision: LLM-Stats cites 3.3x higher-resolution vision processing.
• Effort level: LLM-Stats and ALM Corp both mention a new xhigh effort level.
• Tokenizer: ALM Corp notes an updated tokenizer that can increase token counts for the same input.

Those details can change the practical result of a migration. Tokenizer changes, in particular, are worth checking before budget or throughput assumptions are carried over from an earlier Claude model.

How teams should use these numbers

• If your priority is coding: Start with SWE-bench Verified. The 87.6% figure is the best-corroborated public score in this set.
• If your priority is agentic workflows: Look beyond the benchmark table and test the new xhigh effort level on the kinds of long-running tasks your system actually delegates.
• If your priority is broad reasoning: GPQA is relevant, but the 94.2% figure is less widely corroborated in the available material than SWE-bench Verified.
• If your codebase is multilingual: SWE-bench Multilingual at 80.5% is a useful clue, but it needs follow-up validation.
• If you are planning a production migration: Test context length, tool use, vision inputs, token consumption and latency under realistic load. The context, vision, effort-level and tokenizer changes can all affect how the model performs in practice.

Bottom line

The shortest defensible summary is this: Claude Opus 4.7 is publicly associated with 87.6% on SWE-bench Verified, 94.2% on GPQA, and 80.5% on SWE-bench Multilingual. The SWE-bench Verified score is the strongest public anchor because it is cited in multiple sources.

GPQA and SWE-bench Multilingual add useful context, but the public evidence for them is thinner in this source set. Treat the benchmarks as a shortlist builder—not as the final decision for production use.