Updated October 2, 2026. Rank says Gemini 3.5 Flash 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
Gemini 3.5 Flash has the higher public score, 63.95 versus 39.29, and the 90% score intervals do not overlap. 3 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.
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. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
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.
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
1 category rests 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 (Tiers 1-3)Math
Normalized gap 33.4FrontierMath v2 (Tier 4)Math
Normalized gap 14.6Each 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-4.1 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 64.0Supported · #30/171 | 35.9Supported · #113/171 | Like-for-likeBenchAlign v5.8 lane · 4 vs 2 public rows | Gemini 3.5 Flash leads |
| Instruction following | 84.1#43/125 | 48.9#82/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | 50.7Supported · #42/119 | Not ranked | Not comparableBenchAlign v5.8 lane · 8 vs 1 public rows | Not comparable |
| Coding | 52.3Supported · #39/144 | Not ranked | Not comparableBenchAlign v5.8 lane · 7 vs 1 public rows | Not comparable |
| Reasoning | 62.8#20/27 | 69.2Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.8#6/49 | 51.3Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 61.5#13/16 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 55.1Unranked · 2 rankable rows | 28.0Unranked · 2 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
Modeled costs are equal
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
GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.
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-4.1
1M
Gemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingGPT-4.1
Not sourced
A 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-4.1
Not published
Gemini 3.5 Flash
Not sourced
GPT-4.1
Not sourced
Gemini 3.5 Flash
Not sourced
GPT-4.1
Not sourced
Gemini 3.5 Flash
Not sourced
GPT-4.1
Not sourced
Gemini 3.5 Flash
Reasoning
GPT-4.1
Non-Reasoning
Gemini 3.5 Flash
Proprietary
GPT-4.1
Proprietary
Gemini 3.5 Flash
Proprietary
GPT-4.1
Proprietary
Gemini 3.5 Flash
2026-05-19
GPT-4.1
2025-04-14
Gemini 3.5 Flash has the higher public score, 63.95 versus 39.29, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.
GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.006 on GPT-4.1; repository review costs $0.102 and $0.124; the cache-heavy agent loop costs $0.15 and $0.52. GPT-4.1 has no published cached-input rate, so cached tokens use its listed input rate.
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
Not directly comparable
Toolathlon
Not directly comparable
OSWorld-Verified
Not directly comparable
Finance Agent v2
Not directly comparable
Gert Labs
Shared sourceGemini 3.5 Flash leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
CursorBench 3.2
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMLU
Not directly comparable
GPQA
Not directly comparable
MMLU-ProX
Not directly comparable
IFEval
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3.5 Flash leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 3.5 Flash leads this result
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Last updated October 2, 2026
