Updated October 2, 2026. Rank says GPT-5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
GPT-5.5 has the higher public score estimate, 69.4 versus 63.95, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 20 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 has the higher public coding point estimate, 62.7 to 52.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 has the higher public agentic point estimate, 59.9 to 50.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 (Tier 4)Math
Normalized gap 20.8ARC-AGI-2Reasoning
Normalized gap 12.9FrontierMath v2 (Tiers 1-3)Math
Normalized gap 12.7HLEKnowledge
Normalized gap 12.0CursorBench 3.2Coding
Normalized gap 9.6Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Gemini 3.5 Flash | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.7Supported · #42/119 | 59.9Supported · #22/119 | Like-for-likeBenchAlign v5.8 lane · 8 vs 14 public rows | GPT-5.5 leads · intervals overlap |
| Coding | 52.3Supported · #39/144 | 62.7Supported · #13/144 | Like-for-likeBenchAlign v5.8 lane · 7 vs 10 public rows | GPT-5.5 leads · intervals overlap |
| Knowledge | 64.0Supported · #30/171 | 69.4Supported · #15/171 | Like-for-likeBenchAlign v5.8 lane · 4 vs 6 public rows | GPT-5.5 leads · intervals overlap |
| Reasoning | 62.8#20/27 | 66.2#17/27 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Multimodal | 87.8#6/49 | 71.4#19/49 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Instruction following | 84.1#43/125 | 91.9#7/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 55.1Unranked · 2 rankable rows | 69.4Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
1K fresh input + 500 output tokens
Gemini 3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash
GPT-5.5
Gemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingGPT-5.5
gpt-5.5
OpenAI pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingGemini 3.5 Flash
Not sourced
GPT-5.5
Not sourced
Gemini 3.5 Flash
Not sourced
GPT-5.5
Not sourced
Gemini 3.5 Flash
Not sourced
GPT-5.5
Not sourced
Gemini 3.5 Flash
Reasoning
GPT-5.5
Reasoning
Gemini 3.5 Flash
Proprietary
GPT-5.5
Proprietary
Gemini 3.5 Flash
Proprietary
GPT-5.5
Proprietary
Gemini 3.5 Flash
2026-05-19
GPT-5.5
2026-04-23
GPT-5.5 has the higher public score estimate, 69.4 versus 63.95, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-5.5 has the higher public coding point estimate, 62.7 to 52.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.5 has the higher public agentic tasks point estimate, 59.9 to 50.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.02 on GPT-5.5; repository review costs $0.102 and $0.34; the cache-heavy agent loop costs $0.15 and $0.5. Costs use the listed standard API rates.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
MCP Atlas
Gemini 3.5 Flash leads this result
Toolathlon
Gemini 3.5 Flash leads this result
OSWorld-Verified
GPT-5.5 leads this result
Finance Agent v2
Not directly comparable
Gert Labs
Shared sourceGPT-5.5 leads this result
ResearchClawBench
Shared sourceGemini 3.5 Flash leads this result
Terminal-Bench 2.1 (Vals)
GPT-5.5 leads this result
Terminal-Bench 2.0
Not directly comparable
CyberGym
Not directly comparable
BrowseComp
Not directly comparable
τ²-bench results
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
GPT-5.5 leads this result
Vibe Code Bench
Shared sourceGPT-5.5 leads this result
cursorBench31
Shared sourceGPT-5.5 leads this result
CursorBench 3.2
Shared sourceGPT-5.5 leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash leads this result
SWE-bench (Vals)
GPT-5.5 leads this result
Terminal-Bench 2.0
Not directly comparable
React Native Evals
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
PostTrainBench v1.1
Not directly comparable
MRCRv2
Not directly comparable
MRCR 1M
Not directly comparable
ARC-AGI-2
GPT-5.5 leads this result
MRCR v2 64K-128K
Not directly comparable
MRCR v2 128K-256K
Not directly comparable
ARC-AGI-3
Not directly comparable
CharXiv
Not directly comparable
MMMU-Pro
Gemini 3.5 Flash leads this result
Blueprint-Bench 2
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
OfficeQA Pro
Not directly comparable
GPQA-D
GPT-5.5 leads this result
HLE
GPT-5.5 leads this result
GPQA Diamond (Vals)
GPT-5.5 leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash leads this result
GPQA
Not directly comparable
HLE w/o tools
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.5 leads this result
FrontierMath (legacy)
Not directly comparable
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Last updated October 2, 2026
