Agentic
Like-for-like- Claude Sonnet 5
- 66.0
- Supported · #12/151
- GPT-5.2
- 43.0
- Supported · #108/151
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Claude Sonnet 5 leads
Claude Sonnet 5 vs GPT-5.2
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 5 has the higher public score estimate, 70.76 versus 65.45, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 64.2 to 47.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the public agentic lane, 66 to 43, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Claude Sonnet 5
Claude Sonnet 5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
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.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 5
Claude Sonnet 5 has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GPT-5.2
GPT-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
18 Claude Sonnet 5 only
5 Shared
10 GPT-5.2 only
- Shared results
- 5
- Claude Sonnet 5 only
- 18
- GPT-5.2 only
- 10
- Like-for-like categories
- 3 / 8
1 category rests 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
Like-for-like- Claude Sonnet 5
- 64.2
- Supported · #13/183
- GPT-5.2
- 47.3
- Supported · #91/183
- Basis
- BenchAlign lane · 9 vs 3 public rows
- Reading
- Claude Sonnet 5 leads
Knowledge
Like-for-like- Claude Sonnet 5
- 67.1
- Supported · #20/181
- GPT-5.2
- 62.0
- Supported · #30/181
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Multimodal
Directional only- Claude Sonnet 5
- 77.5
- #13/48
- GPT-5.2
- 66.3
- #23/48
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 76.4
- Unranked · 2 rankable rows
- GPT-5.2
- 53.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.2
- 57.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- GPT-5.2
- 92.3
- #14/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | GPT-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 66.0Supported · #12/151 | 43.0Supported · #108/151 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | Claude Sonnet 5 leads |
| Coding | 64.2Supported · #13/183 | 47.3Supported · #91/183 | Like-for-likeBenchAlign lane · 9 vs 3 public rows | Claude Sonnet 5 leads |
| Knowledge | 67.1Supported · #20/181 | 62.0Supported · #30/181 | Like-for-likeBenchAlign lane · 6 vs 1 public rows | Claude Sonnet 5 leads · intervals overlap |
| Multimodal | 77.5#13/48 | 66.3#23/48 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Reasoning | 76.4Unranked · 2 rankable rows | 53.8Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 57.5Unranked · 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 |
| Instruction following | Not ranked | 92.3#14/120 | 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.
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.
OSWorld-Verified
Agentic
BrowseComp
Agentic
SWE-bench Pro
Coding
CharXiv
Multimodal
SWE-bench Verified
Coding
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-5.2
- $0.00875
- Fits in one request
Claude Sonnet 5 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-5.2
- $0.1295
- Fits in one request
GPT-5.2 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-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
Claude Sonnet 5 has the lower modeled cost
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Sonnet 5GPT-5.2
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
GPT-5.2
400K
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGPT-5.2
Not sourced
Cached-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-5.2
Not published
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewGPT-5.2
Not sourced
Documented outputs
Claude Sonnet 5
GPT-5.2
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGPT-5.2
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
GPT-5.2
Reasoning
Weight access
Claude Sonnet 5
Proprietary
GPT-5.2
Proprietary
License
Claude Sonnet 5
Proprietary
GPT-5.2
Proprietary
Release date
Claude Sonnet 5
2026-06-30
GPT-5.2
2025-12-11
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, 70.76 versus 65.45, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.1295. Cache-heavy agent loop: $0.18 vs $0.525.
- Context tradeoff
- Claude Sonnet 5 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 evidence33 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceGPT-5.2—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGPT-5.2—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceGPT-5.265.8%SourceClaude Sonnet 5 leads this result
HLE w/ tools
Claude Sonnet 557.4%SourceGPT-5.2—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceGPT-5.247.3%SourceClaude Sonnet 5 leads this result
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceGPT-5.2—Not directly comparable
Gert Labs
Claude Sonnet 5—GPT-5.246.54%SourceNot directly comparable
JobBench
Claude Sonnet 5—GPT-5.234.3%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceGPT-5.280%SourceClaude Sonnet 5 leads this result
SWE-bench Pro
Claude Sonnet 563.2%SourceGPT-5.255.6%SourceClaude Sonnet 5 leads this result
SWE Multilingual
Claude Sonnet 578.3%SourceGPT-5.2—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceGPT-5.2—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGPT-5.2—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceGPT-5.2—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceGPT-5.2—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceGPT-5.2—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 579.6%SourceGPT-5.2—Not directly comparable
Vibe Code Bench
Claude Sonnet 5—GPT-5.253.50%SourceNot directly comparable
Reasoning
ARC-AGI-2
Claude Sonnet 5—GPT-5.252.9%SourceNot directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceGPT-5.2—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceGPT-5.2—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceGPT-5.2—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceGPT-5.2—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceGPT-5.2—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceGPT-5.2—Not directly comparable
GPQA
Claude Sonnet 5—GPT-5.292.4%SourceNot directly comparable
Math
Multimodal
CharXiv
Claude Sonnet 588.3%SourceGPT-5.282.1%SourceClaude Sonnet 5 leads this result
CharXiv w/o tools
Claude Sonnet 577%SourceGPT-5.2—Not directly comparable
MMMU-Pro
Claude Sonnet 5—GPT-5.279.5%SourceNot directly comparable
MathVision
Claude Sonnet 5—GPT-5.283.0%SourceNot directly comparable
V*
Claude Sonnet 5—GPT-5.275.9%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Sonnet 5 or GPT-5.2?
Claude Sonnet 5 has the higher public score estimate, 70.76 versus 65.45, 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-5.2?
Claude Sonnet 5 leads the public coding lane, 64.2 to 47.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Claude Sonnet 5 or GPT-5.2?
Claude Sonnet 5 leads the public agentic tasks lane, 66 to 43, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, Claude Sonnet 5 or GPT-5.2?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00875 on GPT-5.2; repository review costs $0.13 and $0.1295; the cache-heavy agent loop costs $0.18 and $0.525. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Sonnet 5 or GPT-5.2?
Claude Sonnet 5 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 4, 2026
Watch Claude Sonnet 5 vs GPT-5.2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.