Agentic
Directional only- Claude Opus 4.6 (Adaptive)
- 53.0
- Estimated · #39/152
- Claude Sonnet 4.6
- 44.0
- Supported · #93/152
- Basis
- BenchAlign lane · 0 vs 9 public rows
- Reading
- Directional only
Anthropic
61.85/100
Estimated · Public rank #52
90% interval 50.3–73.4
Claude Opus 4.6 (Adaptive) vs Claude Sonnet 4.6
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 4.6 has the higher public score estimate, 62.96 versus 61.85, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
Claude Opus 4.6 (Adaptive)
Claude Opus 4.6 (Adaptive) has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.6 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Opus 4.6 (Adaptive) is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Shared
25 Claude Sonnet 4.6 only
- Shared results
- 1
- Claude Opus 4.6 (Adaptive) only
- 0
- Claude Sonnet 4.6 only
- 25
- Like-for-like categories
- 0 / 8
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Coding
Directional only- Claude Opus 4.6 (Adaptive)
- 56.8
- Estimated · #35/151
- Claude Sonnet 4.6
- 52.2
- Supported · #48/151
- Basis
- BenchAlign lane · 1 vs 8 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- 67.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- 55.8
- Supported · #51/183
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- 49.0
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- 54.1
- #33/48
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.6 (Adaptive)
- Not ranked
- Claude Sonnet 4.6
- 48.2
- #86/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.6 (Adaptive) | Claude Sonnet 4.6 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 53.0Estimated · #39/152 | 44.0Supported · #93/152 | Directional onlyBenchAlign lane · 0 vs 9 public rows | Directional only |
| Coding | 56.8Estimated · #35/151 | 52.2Supported · #48/151 | Directional onlyBenchAlign lane · 1 vs 8 public rows | Directional only |
| Reasoning | Not ranked | 67.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 55.8Supported · #51/183 | Not comparableBenchAlign lane · 0 vs 6 public rows | Not comparable |
| Math | Not ranked | 49.0Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 54.1#33/48 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 48.2#86/123 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- Claude Sonnet 4.6
- $0.0105
- Fits in one request
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- Claude Sonnet 4.6
- $0.195
- Fits in one request
Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.6 (Adaptive)
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Claude Sonnet 4.6
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.6 (Adaptive) has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.6 (Adaptive)Claude Sonnet 4.6
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6 (Adaptive)
Claude Sonnet 4.6
200K
API model ID
Claude Opus 4.6 (Adaptive)
claude-opus-4-6
Anthropic model ID documentationClaude Sonnet 4.6
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6 (Adaptive)
No comparable hosted API rate
Claude Sonnet 4.6
Not published
Documented inputs
Claude Opus 4.6 (Adaptive)
text, image
Anthropic model overviewClaude Sonnet 4.6
Not sourced
Documented outputs
Claude Opus 4.6 (Adaptive)
Claude Sonnet 4.6
Not sourced
Provider availability
Claude Opus 4.6 (Adaptive)
Generally Available · Claude API
Anthropic model overviewClaude Sonnet 4.6
Not sourced
Reasoning profile
Claude Opus 4.6 (Adaptive)
Reasoning
Claude Sonnet 4.6
Non-Reasoning
Weight access
Claude Opus 4.6 (Adaptive)
Proprietary
Claude Sonnet 4.6
Proprietary
License
Claude Opus 4.6 (Adaptive)
Proprietary
Claude Sonnet 4.6
Proprietary
Release date
Claude Opus 4.6 (Adaptive)
2026-02-01
Claude Sonnet 4.6
2026-02-01
If you already use one of these models
- Deployment change
- Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- Claude Sonnet 4.6 has the higher public score estimate, 62.96 versus 61.85, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Claude Opus 4.6 (Adaptive) has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.659.1%SourceNot directly comparable
OSWorld-Verified
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.672.1%SourceNot directly comparable
Claw-Eval
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.667.8%SourceNot directly comparable
CyberGym
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.665.2%SourceNot directly comparable
Gert Labs
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.662.92%SourceNot directly comparable
OSWorld 2.0
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.68.3%SourceNot directly comparable
JobBench
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.636.9%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.657.3%SourceNot directly comparable
ApprenticeBench
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.62%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceClaude Opus 4.6 (Adaptive)53.50%Claude Sonnet 4.651.48%Claude Opus 4.6 (Adaptive) leads this result
SWE-bench Verified
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.679.6%SourceNot directly comparable
SWE-Rebench
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.660.7%SourceNot directly comparable
React Native Evals
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.680.6%SourceNot directly comparable
cursorBench31
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.648.8%SourceNot directly comparable
FrontierCode 1.1 Main
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.624.3%SourceNot directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.682.1%SourceNot directly comparable
SWE-bench (Vals)
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.677.4%SourceNot directly comparable
Knowledge
GPQA
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.689.9%SourceNot directly comparable
SuperGPQA
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.695%SourceNot directly comparable
MMLU-Pro
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.679.2%SourceNot directly comparable
HLE
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.649%SourceNot directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.685.6%SourceNot directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.687.3%SourceNot directly comparable
Math
Multimodal
CharXiv
Claude Opus 4.6 (Adaptive)—Claude Sonnet 4.677.4%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Opus 4.6 (Adaptive) or Claude Sonnet 4.6?
Claude Sonnet 4.6 has the higher public score estimate, 62.96 versus 61.85, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.6 (Adaptive) or Claude Sonnet 4.6?
Claude Opus 4.6 (Adaptive) scores higher for coding on the public lane, 56.8 to 52.2. Claude Opus 4.6 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Opus 4.6 (Adaptive) or Claude Sonnet 4.6?
Claude Opus 4.6 (Adaptive) scores higher for agentic tasks on the public lane, 53 to 44. Claude Opus 4.6 (Adaptive) is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Opus 4.6 (Adaptive) or Claude Sonnet 4.6?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Claude Opus 4.6 (Adaptive) or Claude Sonnet 4.6?
Claude Opus 4.6 (Adaptive) has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 10, 2026
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