Anthropic
Claude Opus 4.5
- Benchmark records
- 1
- Snapshot
- 2026-06-30
Independent model comparison
Claude Opus 4.5 vs Claude Sonnet 4.6: evidence side by side
Compare the models using only evidence that can be identified and attributed. Exact benchmark matches appear first; independent facts and user reports remain separate and do not create an overall ranking.
Exact comparison coverage
1 benchmark record share an exact reviewed comparison key.
Evidence strength
Limited exact coverage
The page is useful for inspection but does not yet contain enough pair-native evidence for Search publication.
Models at a glance
Key differences
Anthropic
Claude Sonnet 4.6
- Benchmark records
- 9
- Snapshot
- 2026-07-30
- Release date
- 2026-02-17
| Detail | Claude Opus 4.5 | Claude Sonnet 4.6 |
|---|---|---|
| Provider |
Anthropic |
Anthropic |
| Release date Shown only where an exact reviewed release date is available. |
Not verified |
2026-02-17 |
| Input price Artificial Analysis operational reference; see the attributed details below. |
Not verified |
$3.00 / 1M tokens |
| Output price Artificial Analysis operational reference; see the attributed details below. |
Not verified |
$15.00 / 1M tokens |
| Median output speed Artificial Analysis operational reference; see the attributed details below. |
Not verified |
46.9 tok/s |
| Exact benchmark coverage Only records carrying the same reviewed comparison identity are counted. |
1 shared record |
1 shared record |
Price and speed appear only when an exact reviewed operational reference is attached to that model identity.
Primary comparison evidence
Exact matched benchmark evidence
A shared benchmark name is not enough. A result appears side by side only when both model records carry the same reviewed comparison key. Duplicate or unversioned records are omitted rather than guessed into alignment.
Repository Issue Resolution
Direct OpenHands SWE-Bench outcomes for resolving real repository issues with a pinned OpenHands agent and language-model configuration.
Claude Opus 4.5
76.6%
OpenHands SWE-Bench resolved
Scores are official OpenHands SWE-Bench resolved percentages for exact OpenHands and model configurations, not bare-model scores or SpringPrompt predictions. Twenty-nine exact task sidecars support marginal 95% bootstrap score intervals; five rows explicitly have no interval. No full-cohort rank confidence is claimed.
Configuration: OpenHands v1.8.3 + claude-opus-4-5
OpenHands Index · as of 30 Jun 2026 · OpenHands · SWE-Bench 2026.06.30-3015ac6
Claude Sonnet 4.6
74.4%
OpenHands SWE-Bench resolved
Scores are official OpenHands SWE-Bench resolved percentages for exact OpenHands and model configurations, not bare-model scores or SpringPrompt predictions. Twenty-nine exact task sidecars support marginal 95% bootstrap score intervals; five rows explicitly have no interval. No full-cohort rank confidence is claimed.
Configuration: OpenHands v1.11.5 + claude-sonnet-4-6
OpenHands Index · as of 30 Jun 2026 · OpenHands · SWE-Bench 2026.06.30-3015ac6
Secondary context · not head-to-head
Independent signals for each model
The sources below evaluated or discussed each model independently. Putting them in adjacent columns makes them easier to inspect, but it does not make their metrics, samples, or observations directly comparable.
Attributed third-party facts
Artificial Analysis details
These facts retain their source, date, protocol, and model identity. They are contextual model-level signals—not a Spring Prompt overall score.
Claude Opus 4.5
No reviewed Artificial Analysis record is attached to this model snapshot.
Claude Sonnet 4.6
Input price
$3.00 / 1M tokens
Representative configuration on the cited source page.
Artificial Analysis · as of 16 Jul 2026
Output price
$15.00 / 1M tokens
Representative configuration on the cited source page.
Artificial Analysis · as of 16 Jul 2026
Median output speed
46.9 tok/s
Median output tokens received per second after generation begins; this excludes time to first token and is not end-to-end response latency.
Artificial Analysis · as of 16 Jul 2026
Anecdotal model-level reports
Initial community opinions from Reddit
Recurring themes from each model's declared observation window. They are not a representative survey and are not direct comparisons between these models.
Claude Opus 4.5
No editor-reviewed Reddit opinions paragraph is active for this model snapshot.
Claude Sonnet 4.6
Initial community opinions
The search-indexed launch-window sample highlighted Claude Sonnet 4.6's speed, price, task focus, and occasional strong coding output. Recurring complaints involved superficial reasoning, distraction, instruction failures, hallucinations, and a less conversational style. Reports varied enough that early usefulness appeared closely tied to task size and the surrounding product.
Source venue: Reddit · window: 2026-02-17 to 2026-03-03 · observed items: 4
Independent early-test reports
Early technical field tests from X
Editor-paraphrased reports from independent authors in each model's fixed 14-day launch window. These selectively surfaced anecdotes are not direct head-to-head tests unless they also appear in the dedicated direct-report section above.
Claude Opus 4.5
Early technical field tests
Early Opus 4.5 tests covered spreadsheet-to-presentation work, a repeatable writing task, Claude Code, and a weekend-long iOS build with repeated feature and bug-fix cycles. The practical signal was sustained coherence across varied tasks and successive application changes. These reports are still a small, self-selected sample and do not establish consistency across production codebases.
Source venue: X · window: 2025-11-24 to 2025-12-08 · 2 tests · 2 independent authors · 2 with a method or artifact · identity reviewed
Claude Sonnet 4.6
Early technical field tests
Early Sonnet 4.6 tests covered conditional routing, scheduling, contract workflows, and repeated Claude Code use. Workflow automation looked capable, but a practical coding comparison found that longer reasoning and more tool calls could slow end-to-end completion despite respectable token speed. The launch evidence supports broad utility with a real throughput tradeoff.
Source venue: X · window: 2026-02-17 to 2026-03-03 · 2 tests · 2 independent authors · 2 with a method or artifact · identity reviewed
X field tests remain separate from Reddit community opinions, Artificial Analysis facts, and every scored benchmark.
How to read this comparison
Spring Prompt does not name an overall winner from unrelated benchmark scores, third-party metrics, Reddit opinions, or X field-test reports.
Different benchmarks measure different constructs and may use different populations, prompts, harnesses, model revisions, tools, and score scales. This page does not average those values or infer a winner from source coverage. Test both models on your own production work before making a consequential choice.