Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 59.8 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.
Updated September 29, 2026. Rank says Claude Sonnet 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Claude Sonnet 5 has the higher public score estimate, 66.99 versus 63.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 59.8 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the public agentic lane, 64.6 to 44.4, with Supported evidence for both models and non-overlapping 90% intervals.
1K fresh input + 500 output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.7
Claude Sonnet 5 leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE-bench ProCoding
Normalized gap 9.8OSWorld-VerifiedAgentic
Normalized gap 8.5HLEKnowledge
Normalized gap 4.4SWE-bench VerifiedCoding
Normalized gap 4.4HLE w/o toolsKnowledge
Normalized gap 3.2Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Claude Opus 4.6 | Claude Sonnet 5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.4Supported · #44/117 | 64.6Supported · #12/117 | Like-for-likeBenchAlign v5.7 lane · 10 vs 7 public rows | Claude Sonnet 5 leads |
| Coding | 49.5Supported · #41/142 | 59.8Supported · #19/142 | Like-for-likeBenchAlign v5.7 lane · 8 vs 11 public rows | Claude Sonnet 5 leads · intervals overlap |
| Multimodal | 60.6#30/50 | 78.4#15/50 | Directional onlyProvisional lane · 1 vs 1 weighted rows | Directional only |
| Knowledge | 58.7Estimated · #42/168 | 64.5Supported · #26/168 | Directional onlyBenchAlign v5.7 lane · 9 vs 6 public rows | Directional only |
| Reasoning | 68.3Unranked · 2 rankable rows | 78.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 51.0#79/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 58.5Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Claude Sonnet 5 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5 has the lower modeled cost
Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
Claude Sonnet 5
Claude Opus 4.6
Not sourced
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6
Not published
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingClaude Opus 4.6
Not sourced
Claude Sonnet 5
text, image
Anthropic model overviewClaude Opus 4.6
Not sourced
Claude Sonnet 5
Claude Opus 4.6
Not sourced
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewClaude Opus 4.6
Non-Reasoning
Claude Sonnet 5
Reasoning
Claude Opus 4.6
Proprietary
Claude Sonnet 5
Proprietary
Claude Opus 4.6
Proprietary
Claude Sonnet 5
Proprietary
Claude Opus 4.6
2026-02-01
Claude Sonnet 5
2026-06-30
Claude Sonnet 5 has the higher public score estimate, 66.99 versus 63.62, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Sonnet 5 leads the public coding lane, 59.8 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.
Claude Sonnet 5 leads the public agentic tasks lane, 64.6 to 44.4, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.007 on Claude Sonnet 5; repository review costs $0.325 and $0.13; the cache-heavy agent loop costs $1.35 and $0.18. Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Claude Sonnet 5 leads this result
OSWorld-Verified
Claude Sonnet 5 leads this result
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
ApprenticeBench
Shared sourceClaude Sonnet 5 leads this result
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Claude Sonnet 5 leads this result
SWE-bench Verified*
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Pro
Claude Sonnet 5 leads this result
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Shared sourceClaude Sonnet 5 leads this result
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
cursorBench32
Not directly comparable
VulcanBench CII v1
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
cursorBench40
Not directly comparable
MMMU-Pro
Not directly comparable
ERQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
MedXpertQA (MM)
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
HLE
Claude Sonnet 5 leads this result
HLE w/o tools
Claude Sonnet 5 leads this result
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated September 29, 2026