Coding
Like-for-like- Gemini 2.5 Pro
- 32.0
- Supported · #165/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 2 vs 9 public rows
- Reading
- GPT-5.5 leads
Gemini 2.5 Pro 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 57.98, and the 90% score intervals do not overlap.
6 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 32, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Chat turn cost
1K fresh input + 500 output tokens
Gemini 2.5 Pro
Gemini 2.5 Pro 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
Gemini 2.5 Pro
Gemini 2.5 Pro 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
Gemini 2.5 Pro
Gemini 2.5 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 2.5 Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Gemini 2.5 Pro only
6 Shared
32 GPT-5.5 only
- Shared results
- 6
- Gemini 2.5 Pro only
- 1
- GPT-5.5 only
- 32
- Like-for-like categories
- 2 / 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.
Knowledge
Like-for-like- Gemini 2.5 Pro
- 51.1
- Supported · #84/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Agentic
Directional only- Gemini 2.5 Pro
- 49.3
- Estimated · #70/151
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 1 vs 13 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 2.5 Pro
- 57.7
- #72/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 2.5 Pro
- 68.5
- Unranked · 2 rankable rows
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 35.2
- 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- Gemini 2.5 Pro
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- 70.3
- Unranked · 1 rankable row
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 32.0Supported · #165/183 | 67.7Supported · #8/183 | Like-for-likeBenchAlign lane · 2 vs 9 public rows | GPT-5.5 leads |
| Knowledge | 51.1Supported · #84/181 | 73.3Supported · #7/181 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | GPT-5.5 leads · intervals overlap |
| Agentic | 49.3Estimated · #70/151 | 63.9Supported · #15/151 | Directional onlyBenchAlign lane · 1 vs 13 public rows | Directional only |
| Instruction following | 57.7#72/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 68.5Unranked · 2 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | 35.2Unranked · 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 |
| Multimodal | 70.3Unranked · 1 rankable row | 71.3#19/48 | 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) 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.
FrontierMath v2 (Tiers 1-3)
Math
HLE
Knowledge
FrontierMath v2 (Tier 4)
Math
GPQA
Knowledge
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
- Gemini 2.5 Pro
- $0.00625
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Gemini 2.5 Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 2.5 Pro
- $0.0925
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Gemini 2.5 Pro has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 2.5 Pro
- $0.15
- Fits in one request
- GPT-5.5
- $0.5
- Fits in one request
Gemini 2.5 Pro has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 2.5 ProGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
GPT-5.5
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingGPT-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.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 2.5 Pro
Not sourced
GPT-5.5
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
GPT-5.5
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
GPT-5.5
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
GPT-5.5
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
GPT-5.5
Proprietary
License
Gemini 2.5 Pro
Proprietary
GPT-5.5
Proprietary
Release date
Gemini 2.5 Pro
2025-03-01
GPT-5.5
2026-04-23
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-5.5 has the higher public score, 73.27 versus 57.98, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0925 vs $0.34. Cache-heavy agent loop: $0.15 vs $0.5.
- Context tradeoff
- Both models list 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 evidence39 rows
Agentic
Gert Labs
Shared sourceGemini 2.5 Pro42.01%GPT-5.572.93%GPT-5.5 leads this result
Terminal-Bench 2.0
Gemini 2.5 Pro—GPT-5.582%SourceNot directly comparable
CyberGym
Gemini 2.5 Pro—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Gemini 2.5 Pro—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Gemini 2.5 Pro—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Gemini 2.5 Pro—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Gemini 2.5 Pro—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Gemini 2.5 Pro—GPT-5.598%SourceNot directly comparable
ResearchClawBench
Gemini 2.5 Pro—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Gemini 2.5 Pro—GPT-5.513.0%SourceNot directly comparable
JobBench
Gemini 2.5 Pro—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Gemini 2.5 Pro—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 2.5 Pro—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceGPT-5.5—Not directly comparable
Vibe Code Bench
Shared sourceGemini 2.5 Pro0.40%GPT-5.569.85%GPT-5.5 leads this result
SWE-bench Pro
Gemini 2.5 Pro—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 2.5 Pro—GPT-5.582.0%SourceNot directly comparable
React Native Evals
Gemini 2.5 Pro—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Gemini 2.5 Pro—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Gemini 2.5 Pro—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Gemini 2.5 Pro—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 2.5 Pro—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
Gemini 2.5 Pro—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Gemini 2.5 Pro83%SourceGPT-5.593.6%SourceGPT-5.5 leads this result
HLE
Gemini 2.5 Pro18.8%SourceGPT-5.552.2%SourceGPT-5.5 leads this result
GPQA-D
Gemini 2.5 Pro—GPT-5.593.6%SourceNot directly comparable
HLE w/o tools
Gemini 2.5 Pro—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
Gemini 2.5 Pro—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
Gemini 2.5 Pro—GPT-5.588.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 2.5 Pro14.138%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 2.5 Pro4.167%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
Gemini 2.5 Pro—GPT-5.551.7%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Gemini 2.5 Pro or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 57.98, 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, Gemini 2.5 Pro or GPT-5.5?
GPT-5.5 leads the public coding lane, 67.7 to 32, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, Gemini 2.5 Pro or GPT-5.5?
GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 49.3. Gemini 2.5 Pro 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, Gemini 2.5 Pro or GPT-5.5?
For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.02 on GPT-5.5; repository review costs $0.0925 and $0.34; the cache-heavy agent loop costs $0.15 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, Gemini 2.5 Pro or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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