Agentic
Like-for-like- GPT-5.2
- 43.0
- Supported · #108/151
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 4 vs 13 public rows
- Reading
- GPT-5.5 leads
GPT-5.2 vs GPT-5.5
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 73.27 versus 65.45, and the 90% score intervals do not overlap.
11 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
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.7 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
GPT-5.5
GPT-5.5 leads on the public agentic lane, 63.9 to 43, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.5
GPT-5.5 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.
4 GPT-5.2 only
11 Shared
27 GPT-5.5 only
- Shared results
- 11
- GPT-5.2 only
- 4
- GPT-5.5 only
- 27
- Like-for-like categories
- 3 / 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
Like-for-like- GPT-5.2
- 47.3
- Supported · #91/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 3 vs 9 public rows
- Reading
- GPT-5.5 leads
Knowledge
Like-for-like- GPT-5.2
- 62.0
- Supported · #30/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 1 vs 6 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Multimodal
Directional only- GPT-5.2
- 66.3
- #23/48
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.2
- 92.3
- #14/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.2
- 53.8
- Unranked · 3 rankable rows
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 57.5
- Unranked · 2 rankable rows
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2 | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.0Supported · #108/151 | 63.9Supported · #15/151 | Like-for-likeBenchAlign lane · 4 vs 13 public rows | GPT-5.5 leads |
| Coding | 47.3Supported · #91/183 | 67.7Supported · #8/183 | Like-for-likeBenchAlign lane · 3 vs 9 public rows | GPT-5.5 leads |
| Knowledge | 62.0Supported · #30/181 | 73.3Supported · #7/181 | Like-for-likeBenchAlign lane · 1 vs 6 public rows | GPT-5.5 leads · intervals overlap |
| Multimodal | 66.3#23/48 | 71.3#19/48 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Instruction following | 92.3#14/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 53.8Unranked · 3 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
| Math | 57.5Unranked · 2 rankable rows | 69.6Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | 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.
ARC-AGI-2
Reasoning
OSWorld-Verified
Agentic
BrowseComp
Agentic
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
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
- GPT-5.2
- $0.00875
- Fits in one request
- GPT-5.5
- $0.02
- 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
- GPT-5.2
- $0.1295
- Fits in one request
- GPT-5.5
- $0.34
- 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
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
GPT-5.5 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.
SpecificationGPT-5.2GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
GPT-5.5
API model ID
GPT-5.2
Not sourced
GPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2
Not published
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.2
Not sourced
GPT-5.5
Not sourced
Documented outputs
GPT-5.2
Not sourced
GPT-5.5
Not sourced
Provider availability
GPT-5.2
Not sourced
GPT-5.5
Not sourced
Reasoning profile
GPT-5.2
Reasoning
GPT-5.5
Reasoning
Weight access
GPT-5.2
Proprietary
GPT-5.5
Proprietary
License
GPT-5.2
Proprietary
GPT-5.5
Proprietary
Release date
GPT-5.2
2025-12-11
GPT-5.5
2026-04-23
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.5 has the higher public score, 73.27 versus 65.45, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.1295 vs $0.34. Cache-heavy agent loop: $0.525 vs $0.5.
- Context tradeoff
- GPT-5.5 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 evidence42 rows
Agentic
BrowseComp
GPT-5.265.8%SourceGPT-5.584.4%SourceGPT-5.5 leads this result
OSWorld-Verified
GPT-5.247.3%SourceGPT-5.578.7%SourceGPT-5.5 leads this result
Gert Labs
Shared sourceGPT-5.246.54%GPT-5.572.93%GPT-5.5 leads this result
JobBench
Shared sourceGPT-5.234.3%GPT-5.542.7%GPT-5.5 leads this result
Terminal-Bench 2.0
GPT-5.2—GPT-5.582%SourceNot directly comparable
CyberGym
GPT-5.2—GPT-5.581.8%SourceNot directly comparable
MCP Atlas
GPT-5.2—GPT-5.575.3%SourceNot directly comparable
Toolathlon
GPT-5.2—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
GPT-5.2—GPT-5.598%SourceNot directly comparable
ResearchClawBench
GPT-5.2—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
GPT-5.2—GPT-5.513.0%SourceNot directly comparable
ExploitGym
GPT-5.2—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.2—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
GPT-5.255.6%SourceGPT-5.558.6%SourceGPT-5.5 leads this result
Vibe Code Bench
Shared sourceGPT-5.253.50%GPT-5.569.85%GPT-5.5 leads this result
Terminal-Bench 2.0
GPT-5.2—GPT-5.582.0%SourceNot directly comparable
React Native Evals
GPT-5.2—GPT-5.584.7%SourceNot directly comparable
cursorBench31
GPT-5.2—GPT-5.559.2%SourceNot directly comparable
cursorBench32
GPT-5.2—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
GPT-5.2—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
GPT-5.2—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
GPT-5.2—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.292.4%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
GPQA-D
GPT-5.2—GPT-5.593.6%SourceNot directly comparable
HLE
GPT-5.2—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
GPT-5.2—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-5.2—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-5.2—GPT-5.588.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.240.700%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.218.800%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
GPT-5.2—GPT-5.551.7%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.279.5%SourceGPT-5.581.2%SourceGPT-5.5 leads this result
MathVision
GPT-5.283.0%SourceGPT-5.5—Not directly comparable
CharXiv
GPT-5.282.1%SourceGPT-5.5—Not directly comparable
V*
GPT-5.275.9%SourceGPT-5.5—Not directly comparable
MMMU-Pro w/ Python
GPT-5.2—GPT-5.583.2%SourceNot directly comparable
OfficeQA Pro
GPT-5.2—GPT-5.554.1%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.2 or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 65.45, 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, GPT-5.2 or GPT-5.5?
GPT-5.5 leads the public coding lane, 67.7 to 47.3, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.2 or GPT-5.5?
GPT-5.5 leads the public agentic tasks lane, 63.9 to 43, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GPT-5.2 or GPT-5.5?
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.02 on GPT-5.5; repository review costs $0.1295 and $0.34; the cache-heavy agent loop costs $0.525 and $0.5. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.2 or GPT-5.5?
GPT-5.5 has the larger documented context window: 1M, compared with 400K.
Related comparisons
Last updated September 4, 2026
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