Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Luna
GPT-5.6 Luna leads on the public coding lane, 63.2 to 59.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Claude Sonnet 5 vs GPT-5.6 Luna
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
Share or export
Decision reading
Claude Sonnet 5 has the higher public score estimate, 66.99 versus 65.55, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 12 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 12
- Claude Sonnet 5 only
- 14
- GPT-5.6 Luna only
- 18
- Like-for-like categories
- 3 / 8
Supported: Claude Sonnet 5 and GPT-5.6 LunaHow the comparison works
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.
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the public agentic lane, 64.6 to 55.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Which one for a specific job
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.
59.8Claude Sonnet 563.2GPT-5.6 Luna
Like-for-like · BenchAlign v5.7
GPT-5.6 Luna 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
14 Claude Sonnet 5 only
12 Shared
18 GPT-5.6 Luna only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 1.5Claude Sonnet 5:87.5%GPT-5.6 Luna:86.0%BrowseCompAgentic
Normalized gap 1.4Claude Sonnet 5:84.7%GPT-5.6 Luna:83.3%SWE-bench ProCoding
Normalized gap 0.5Claude Sonnet 5:63.2%GPT-5.6 Luna:62.7%cursorBench32Coding
Normalized gap 0.4Claude Sonnet 5:61.5%GPT-5.6 Luna:61.1%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each 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.
Agentic
Like-for-like- Claude Sonnet 5
- 64.6
- Supported · #12/117
- GPT-5.6 Luna
- 55.2
- Supported · #33/117
- Basis
- BenchAlign v5.7 lane · 7 vs 9 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Coding
Like-for-like- Claude Sonnet 5
- 59.8
- Supported · #19/142
- GPT-5.6 Luna
- 63.2
- Supported · #11/142
- Basis
- BenchAlign v5.7 lane · 11 vs 8 public rows
- Reading
- GPT-5.6 Luna leads · intervals overlap
Knowledge
Like-for-like- Claude Sonnet 5
- 64.5
- Supported · #26/168
- GPT-5.6 Luna
- 64.7
- Supported · #25/168
- Basis
- BenchAlign v5.7 lane · 6 vs 6 public rows
- Reading
- Practical tie
Multimodal
Directional only- Claude Sonnet 5
- 78.4
- #15/50
- GPT-5.6 Luna
- 68.1
- #22/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 78.7
- Unranked · 2 rankable rows
- GPT-5.6 Luna
- 55.8
- #24/27
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.6 Luna
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.6 Luna
- 94.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | GPT-5.6 Luna | Basis | Reading |
|---|---|---|---|---|
| Agentic | 64.6Supported · #12/117 | 55.2Supported · #33/117 | Like-for-likeBenchAlign v5.7 lane · 7 vs 9 public rows | Claude Sonnet 5 leads · intervals overlap |
| Coding | 59.8Supported · #19/142 | 63.2Supported · #11/142 | Like-for-likeBenchAlign v5.7 lane · 11 vs 8 public rows | GPT-5.6 Luna leads · intervals overlap |
| Knowledge | 64.5Supported · #26/168 | 64.7Supported · #25/168 | Like-for-likeBenchAlign v5.7 lane · 6 vs 6 public rows | Practical tie |
| Multimodal | 78.4#15/50 | 68.1#22/50 | Directional onlyProvisional lane · 1 vs 1 weighted rows | Directional only |
| Reasoning | 78.7Unranked · 2 rankable rows | 55.8#24/27 | 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 |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 94.2Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 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.
Supported evidence per lane · bars run 0–100Methodology
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 Sonnet 5
- $0.007
- Fits in one request
- GPT-5.6 Luna
- $0.0008
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 5
- $0.13
- Fits in one request
- GPT-5.6 Luna
- $0.0136
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 5
- $0.18
- Fits in one request
- GPT-5.6 Luna
- $0.02
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Sonnet 5GPT-5.6 Luna
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
GPT-5.6 Luna
1.05M
OpenAI model catalogAPI model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingGPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Sonnet 5
text, image
Anthropic model overviewGPT-5.6 Luna
text, image
OpenAI model catalogDocumented outputs
Claude Sonnet 5
GPT-5.6 Luna
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
Claude Sonnet 5
Reasoning
GPT-5.6 Luna
Reasoning
Weight access
Claude Sonnet 5
Proprietary
GPT-5.6 Luna
Proprietary
License
Claude Sonnet 5
Proprietary
GPT-5.6 Luna
Proprietary
Release date
Claude Sonnet 5
2026-06-30
GPT-5.6 Luna
2026-07-09
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Claude Sonnet 5 has the higher public score estimate, 66.99 versus 65.55, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.0136. Cache-heavy agent loop: $0.18 vs $0.02.
- Context tradeoff
- GPT-5.6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Sonnet 5 or GPT-5.6 Luna?
Claude Sonnet 5 has the higher public score estimate, 66.99 versus 65.55, 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 Sonnet 5 or GPT-5.6 Luna?
GPT-5.6 Luna leads the public coding lane, 63.2 to 59.8, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Sonnet 5 or GPT-5.6 Luna?
Claude Sonnet 5 leads the public agentic tasks lane, 64.6 to 55.2, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Sonnet 5 or GPT-5.6 Luna?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.0008 on GPT-5.6 Luna; repository review costs $0.13 and $0.0136; the cache-heavy agent loop costs $0.18 and $0.02. Costs use the listed standard API rates.
Which has the larger context window, Claude Sonnet 5 or GPT-5.6 Luna?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence44 rows
Agentic
Terminal-Bench 3.0
Shared sourceClaude Sonnet 514.6%GPT-5.6 Luna14.3%Claude Sonnet 5 leads this result
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
BrowseComp
Claude Sonnet 584.7%SourceGPT-5.6 Luna83.3%SourceClaude Sonnet 5 leads this result
HLE w/ tools
Claude Sonnet 557.4%SourceGPT-5.6 Luna—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceGPT-5.6 Luna—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceGPT-5.6 Luna79.0%SourceGPT-5.6 Luna leads this result
ApprenticeBench
Shared sourceClaude Sonnet 516%GPT-5.6 Luna7%Claude Sonnet 5 leads this result
OSWorld 2.0
Claude Sonnet 5—GPT-5.6 Luna45.6%SourceNot directly comparable
CyberGym
Claude Sonnet 5—GPT-5.6 Luna77.9%SourceNot directly comparable
ExploitGym
Claude Sonnet 5—GPT-5.6 Luna12.4%SourceNot directly comparable
Toolathlon
Claude Sonnet 5—GPT-5.6 Luna53.4%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceGPT-5.6 Luna—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceGPT-5.6 Luna62.7%SourceClaude Sonnet 5 leads this result
SWE Multilingual
Claude Sonnet 578.3%SourceGPT-5.6 Luna—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceGPT-5.6 Luna—Not directly comparable
Terminal-Bench 2.1
Claude Sonnet 580.4%SourceGPT-5.6 Luna84.7%SourceGPT-5.6 Luna leads this result
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceGPT-5.6 Luna—Not directly comparable
cursorBench32
Shared sourceClaude Sonnet 561.5%GPT-5.6 Luna61.1%Claude Sonnet 5 leads this result
VulcanBench CII v1
Claude Sonnet 589.2%SourceGPT-5.6 Luna—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceGPT-5.6 Luna—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 579.6%SourceGPT-5.6 Luna93.0%SourceGPT-5.6 Luna leads this result
cursorBench40
Shared sourceClaude Sonnet 534.1%GPT-5.6 Luna35.9%GPT-5.6 Luna leads this result
DeepSWE
Claude Sonnet 5—GPT-5.6 Luna67.2%SourceNot directly comparable
FrontierCode 1.1 Extended
Claude Sonnet 5—GPT-5.6 Luna55.1%SourceNot directly comparable
VulcanBench v3
Claude Sonnet 5—GPT-5.6 Luna85.5%SourceNot directly comparable
Reasoning
Multimodal
CharXiv
Claude Sonnet 588.3%SourceGPT-5.6 Luna—Not directly comparable
CharXiv w/o tools
Claude Sonnet 577%SourceGPT-5.6 Luna—Not directly comparable
MMMU-Pro
Claude Sonnet 5—GPT-5.6 Luna78.4%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Sonnet 5—GPT-5.6 Luna79.5%SourceNot directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceGPT-5.6 Luna—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceGPT-5.6 Luna—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceGPT-5.6 Luna—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceGPT-5.6 Luna—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceGPT-5.6 Luna91.7%SourceGPT-5.6 Luna leads this result
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceGPT-5.6 Luna86.0%SourceClaude Sonnet 5 leads this result
GPQA
Claude Sonnet 5—GPT-5.6 Luna92.3%SourceNot directly comparable
GPQA-D
Claude Sonnet 5—GPT-5.6 Luna92.3%SourceNot directly comparable
HealthBench Professional
Claude Sonnet 5—GPT-5.6 Luna55.7%SourceNot directly comparable
HealthBench Hard
Claude Sonnet 5—GPT-5.6 Luna32.0%SourceNot directly comparable
Math
44 public results · 12 shared
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Last updated September 29, 2026