Coding
Directional only- Claude 4 Sonnet
- 72.7
- GPT-5.2
- 70.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Model comparison
Claude 4 Sonnet vs GPT-5.2
Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.2 has the higher public score, 57.77 versus 41.85, and the 90% score intervals do not overlap.
3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
GPT-5.2
GPT-5.2 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 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
Show secondary and unsupported calls
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Shared
12 GPT-5.2 only
- Shared results
- 3
- Claude 4 Sonnet only
- 0
- GPT-5.2 only
- 12
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- 55.7
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- 52.9
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- 92.4
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- 35.2
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- 80.4
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude 4 Sonnet
- Not measured
- GPT-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude 4 Sonnet | GPT-5.2 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 72.7 | 70.6 | Directional only1 vs 2 rows | Directional only |
| Agentic | Not measured | 55.7 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | 52.9 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 92.4 | Not comparable0 vs 1 rows | Not comparable |
| Math | Not measured | 35.2 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 80.4 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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 4 Sonnet
- $0.0105
- Fits in one request
- GPT-5.2
- $0.00875
- Fits in one request
GPT-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude 4 Sonnet
- $0.195
- 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 4 Sonnet
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. 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 4 SonnetGPT-5.2
Context window
Maximum documented context; output-token limits may be lower.
Claude 4 Sonnet
200K
GPT-5.2
400K
API model ID
Claude 4 Sonnet
Not sourced
GPT-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 4 Sonnet
Not published
GPT-5.2
Not published
Documented inputs
Claude 4 Sonnet
Not sourced
GPT-5.2
Not sourced
Documented outputs
Claude 4 Sonnet
Not sourced
GPT-5.2
Not sourced
Provider availability
Claude 4 Sonnet
Not sourced
GPT-5.2
Not sourced
Reasoning profile
Claude 4 Sonnet
Non-Reasoning
GPT-5.2
Reasoning
Weight access
Claude 4 Sonnet
Proprietary
GPT-5.2
Proprietary
License
Claude 4 Sonnet
Proprietary
GPT-5.2
Proprietary
Release date
Claude 4 Sonnet
2025-05-01
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
- GPT-5.2 has the higher public score, 57.77 versus 41.85, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.195 vs $0.1295. Cache-heavy agent loop: $0.81 vs $0.525.
- Context tradeoff
- GPT-5.2 has the larger documented window (400K).
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 evidence15 rows
Agentic
Gert Labs
Shared sourceClaude 4 Sonnet39.66%GPT-5.246.54%GPT-5.2 leads this result
JobBench
Shared sourceClaude 4 Sonnet18.4%GPT-5.234.3%GPT-5.2 leads this result
BrowseComp
Claude 4 Sonnet—GPT-5.265.8%SourceNot directly comparable
OSWorld-Verified
Claude 4 Sonnet—GPT-5.247.3%SourceNot directly comparable
Coding
Reasoning
ARC-AGI-2
Claude 4 Sonnet—GPT-5.252.9%SourceNot directly comparable
Knowledge
GPQA
Claude 4 Sonnet—GPT-5.292.4%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Claude 4 Sonnet or GPT-5.2?
GPT-5.2 has the higher public score, 57.77 versus 41.85, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Claude 4 Sonnet or GPT-5.2?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude 4 Sonnet or GPT-5.2?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude 4 Sonnet or GPT-5.2?
For the stated presets, chat costs $0.0105 on Claude 4 Sonnet and $0.00875 on GPT-5.2; repository review costs $0.195 and $0.1295; the cache-heavy agent loop costs $0.81 and $0.525. Claude 4 Sonnet does not fit this workload in one request. Claude 4 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude 4 Sonnet or GPT-5.2?
GPT-5.2 has the larger documented context window: 400K, compared with 200K.
Related comparisons
Last updated August 7, 2026
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