Coding
Directional only- Gemini 2.5 Flash
- 49.5
- Estimated · #78/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 0 vs 9 public rows
- Reading
- Directional only
Gemini 2.5 Flash 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 51.68, and the 90% score intervals do not overlap.
2 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.
Chat turn cost
1K fresh input + 500 output tokens
Gemini 2.5 Flash
Gemini 2.5 Flash 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 Flash
Gemini 2.5 Flash 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 Flash
Gemini 2.5 Flash 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
Gemini 2.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 2.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
2 Shared
36 GPT-5.5 only
- Shared results
- 2
- Gemini 2.5 Flash only
- 0
- GPT-5.5 only
- 36
- Like-for-like categories
- 0 / 8
3 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
Directional only- Gemini 2.5 Flash
- 45.8
- Estimated · #113/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 2.5 Flash
- 45.1
- #91/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Gemini 2.5 Flash
- Not ranked
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 0 vs 13 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Flash
- 55.5
- 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 Flash
- 28.7
- 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 Flash
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Flash
- 56.5
- 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 Flash | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 49.5Estimated · #78/183 | 67.7Supported · #8/183 | Directional onlyBenchAlign lane · 0 vs 9 public rows | Directional only |
| Knowledge | 45.8Estimated · #113/181 | 73.3Supported · #7/181 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Instruction following | 45.1#91/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 63.9Supported · #15/151 | Not comparableBenchAlign lane · 0 vs 13 public rows | Not comparable |
| Reasoning | 55.5Unranked · 2 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | 28.7Unranked · 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 | 56.5Unranked · 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
FrontierMath v2 (Tier 4)
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
- Gemini 2.5 Flash
- $0.00155
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Gemini 2.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 2.5 Flash
- $0.0225
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Gemini 2.5 Flash 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 Flash
- $0.037
- Fits in one request
- GPT-5.5
- $0.5
- Fits in one request
Gemini 2.5 Flash 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 FlashGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Flash
GPT-5.5
API model ID
Gemini 2.5 Flash
gemini-2.5-flash
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 Flash
$0.03 per 1M cached input tokens
Google Gemini API pricingGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 2.5 Flash
Not sourced
GPT-5.5
Not sourced
Documented outputs
Gemini 2.5 Flash
Not sourced
GPT-5.5
Not sourced
Provider availability
Gemini 2.5 Flash
Not sourced
GPT-5.5
Not sourced
Reasoning profile
Gemini 2.5 Flash
Non-Reasoning
GPT-5.5
Reasoning
Weight access
Gemini 2.5 Flash
Proprietary
GPT-5.5
Proprietary
License
Gemini 2.5 Flash
Proprietary
GPT-5.5
Proprietary
Release date
Gemini 2.5 Flash
2025-06-17
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 51.68, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0225 vs $0.34. Cache-heavy agent loop: $0.037 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 evidence38 rows
Agentic
Terminal-Bench 2.0
Gemini 2.5 Flash—GPT-5.582%SourceNot directly comparable
CyberGym
Gemini 2.5 Flash—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Gemini 2.5 Flash—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Gemini 2.5 Flash—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Gemini 2.5 Flash—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Gemini 2.5 Flash—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Gemini 2.5 Flash—GPT-5.598%SourceNot directly comparable
Gert Labs
Gemini 2.5 Flash—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Gemini 2.5 Flash—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Gemini 2.5 Flash—GPT-5.513.0%SourceNot directly comparable
JobBench
Gemini 2.5 Flash—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Gemini 2.5 Flash—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 2.5 Flash—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Pro
Gemini 2.5 Flash—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 2.5 Flash—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
Gemini 2.5 Flash—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Gemini 2.5 Flash—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Gemini 2.5 Flash—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Gemini 2.5 Flash—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Gemini 2.5 Flash—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 2.5 Flash—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
Gemini 2.5 Flash—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Gemini 2.5 Flash—GPT-5.593.6%SourceNot directly comparable
GPQA-D
Gemini 2.5 Flash—GPT-5.593.6%SourceNot directly comparable
HLE
Gemini 2.5 Flash—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Gemini 2.5 Flash—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
Gemini 2.5 Flash—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
Gemini 2.5 Flash—GPT-5.588.1%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 2.5 Flash4.844%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 2.5 Flash4.167%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
Gemini 2.5 Flash—GPT-5.551.7%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Gemini 2.5 Flash or GPT-5.5?
GPT-5.5 has the higher public score, 73.27 versus 51.68, 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 Flash or GPT-5.5?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 49.5. Gemini 2.5 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 2.5 Flash or GPT-5.5?
Gemini 2.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Gemini 2.5 Flash or GPT-5.5?
For the stated presets, chat costs $0.00155 on Gemini 2.5 Flash and $0.02 on GPT-5.5; repository review costs $0.0225 and $0.34; the cache-heavy agent loop costs $0.037 and $0.5. Costs use the listed standard API rates.
Which has the larger context window, Gemini 2.5 Flash or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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