Agentic
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- 81.6
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Gemini 3.7 Flash vs GPT-5.5
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 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.
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 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 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Gemini 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.7 Flash
Gemini 3.7 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
No shared weighted benchmark basis supports a winner.
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
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.
13 Gemini 3.7 Flash only
2 Shared
36 GPT-5.5 only
- Shared results
- 2
- Gemini 3.7 Flash only
- 13
- GPT-5.5 only
- 36
- Like-for-like categories
- 0 / 8
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.
Coding
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- 58.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- 85.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- 57.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- 47.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.7 Flash
- 88.7
- GPT-5.5
- 70.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.7 Flash
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 3.7 Flash | GPT-5.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 81.6 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 58.6 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | 85.0 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 57.8 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 47.6 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 88.7 | 70.4 | Not comparable1 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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 3.7 Flash
- $0.00262
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.7 Flash
- $0.04875
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Gemini 3.7 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 3.7 Flash
- $0.2025
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
Gemini 3.7 Flash has the lower modeled cost
Gemini 3.7 Flash 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.
SpecificationGemini 3.7 FlashGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.7 Flash
GPT-5.5
API model ID
Gemini 3.7 Flash
gemini-3.7-flash
Google Gemini 3.7 Flash model documentationGPT-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 3.7 Flash
Not published
Google: Introducing Gemini 3.7 FlashGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Gemini 3.7 Flash
text, image, video, audio, pdf
Google Gemini 3.7 Flash model documentationGPT-5.5
Not sourced
Documented outputs
Gemini 3.7 Flash
GPT-5.5
Not sourced
Provider availability
Gemini 3.7 Flash
Generally Available · Gemini API, Google AI Studio, Google Antigravity, Gemini Enterprise Agent Platform, Gemini Enterprise, Gemini app via Spark
Google Gemini 3.7 Flash launchGPT-5.5
Not sourced
Reasoning profile
Gemini 3.7 Flash
Reasoning
GPT-5.5
Reasoning
Weight access
Gemini 3.7 Flash
Proprietary
GPT-5.5
Proprietary
License
Gemini 3.7 Flash
Proprietary
GPT-5.5
Proprietary
Release date
Gemini 3.7 Flash
2026-08-13
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.04875 vs $0.34. Cache-heavy agent loop: $0.2025 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 evidence51 rows
Agentic
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceGPT-5.5—Not directly comparable
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%SourceGPT-5.5—Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%SourceGPT-5.5—Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%SourceGPT-5.513.0%SourceGemini 3.7 Flash leads this result
Agents' Last Exam
Gemini 3.7 Flash26.3%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.0
Gemini 3.7 Flash—GPT-5.582%SourceNot directly comparable
CyberGym
Gemini 3.7 Flash—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Gemini 3.7 Flash—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Gemini 3.7 Flash—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Gemini 3.7 Flash—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Gemini 3.7 Flash—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Gemini 3.7 Flash—GPT-5.598%SourceNot directly comparable
Gert Labs
Gemini 3.7 Flash—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Gemini 3.7 Flash—GPT-5.517.0%SourceNot directly comparable
JobBench
Gemini 3.7 Flash—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Gemini 3.7 Flash—GPT-5.513.4%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%SourceGPT-5.543.0%SourceGemini 3.7 Flash leads this result
deepSwe
Gemini 3.7 Flash65.3%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
Gemini 3.7 Flash—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3.7 Flash—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
Gemini 3.7 Flash—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Gemini 3.7 Flash—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Gemini 3.7 Flash—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Gemini 3.7 Flash—GPT-5.558.4%SourceNot directly comparable
APEX-SWE
Gemini 3.7 Flash—GPT-5.537.0%SourceNot directly comparable
EEBench
Gemini 3.7 Flash—GPT-5.542.3%SourceNot directly comparable
CADGenBench Generation
Gemini 3.7 Flash—GPT-5.529.7%SourceNot directly comparable
SpaceXAI MTS Eval
Gemini 3.7 Flash—GPT-5.546.4%SourceNot directly comparable
InferenceEval
Gemini 3.7 Flash—GPT-5.538.9%SourceNot directly comparable
Reasoning
GDM-MRCR v2 128K average
Gemini 3.7 Flash97%SourceGPT-5.5—Not directly comparable
MRCR v2 64K-128K
Gemini 3.7 Flash—GPT-5.583.1%SourceNot directly comparable
MRCR v2 128K-256K
Gemini 3.7 Flash—GPT-5.587.5%SourceNot directly comparable
ARC-AGI-2
Gemini 3.7 Flash—GPT-5.585%SourceNot directly comparable
ARC-AGI-3
Gemini 3.7 Flash—GPT-5.50.4%SourceNot directly comparable
Knowledge
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%SourceGPT-5.5—Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%SourceGPT-5.5—Not directly comparable
GPQA
Gemini 3.7 Flash—GPT-5.593.6%SourceNot directly comparable
GPQA-D
Gemini 3.7 Flash—GPT-5.593.6%SourceNot directly comparable
HLE
Gemini 3.7 Flash—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Gemini 3.7 Flash—GPT-5.541.4%SourceNot directly comparable
Math
Multimodal
GDP.pdf (no tools)
Gemini 3.7 Flash34.0%SourceGPT-5.5—Not directly comparable
CharXiv w/o tools
Gemini 3.7 Flash84.5%SourceGPT-5.5—Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%SourceGPT-5.5—Not directly comparable
LVBench
Gemini 3.7 Flash85.4%SourceGPT-5.5—Not directly comparable
MMMU-Pro
Gemini 3.7 Flash—GPT-5.581.2%SourceNot directly comparable
MMMU-Pro w/ Python
Gemini 3.7 Flash—GPT-5.583.2%SourceNot directly comparable
OfficeQA Pro
Gemini 3.7 Flash—GPT-5.554.1%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.7 Flash or GPT-5.5?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3.7 Flash or GPT-5.5?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Gemini 3.7 Flash or GPT-5.5?
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, Gemini 3.7 Flash or GPT-5.5?
For the stated presets, chat costs $0.00263 on Gemini 3.7 Flash and $0.02 on GPT-5.5; repository review costs $0.04875 and $0.34; the cache-heavy agent loop costs $0.2025 and $0.5. Gemini 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3.7 Flash or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
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