Updated October 2, 2026. Rank says Gemini 3.1 Pro 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 3.1 Pro has the higher public point estimate, 65.09 versus 63.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. 14 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
Gemini 3.5 Flash
Gemini 3.5 Flash has the higher public coding point estimate, 52.3 to 42.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Gemini 3.5 Flash
Gemini 3.5 Flash has the higher public agentic point estimate, 50.7 to 39.3, 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
Gemini 3.5 Flash 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.
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.
ARC-AGI-2Reasoning
Normalized gap 5.0CharXivMultimodal
Normalized gap 4.0FrontierMath v2 (Tier 4)Math
Normalized gap 2.1FrontierMath v2 (Tiers 1-3)Math
Normalized gap 2.1MMLU-Pro (Vals)Knowledge
Normalized gap 1.5Each 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 3.1 Pro | Gemini 3.5 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 39.3Supported · #59/119 | 50.7Supported · #42/119 | Like-for-likeBenchAlign v5.8 lane · 6 vs 8 public rows | Gemini 3.5 Flash leads · intervals overlap |
| Coding | 42.3Supported · #61/144 | 52.3Supported · #39/144 | Like-for-likeBenchAlign v5.8 lane · 6 vs 7 public rows | Gemini 3.5 Flash leads · intervals overlap |
| Multimodal | 80.1#13/49 | 87.8#6/49 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Gemini 3.5 Flash leads |
| Knowledge | 66.2Supported · #22/171 | 64.0Supported · #30/171 | Like-for-likeBenchAlign v5.8 lane · 6 vs 4 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Reasoning | 54.4Unranked · 2 rankable rows | 62.8#20/27 | 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 |
| Instruction following | Not ranked | 84.1#43/125 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 54.2Unranked · 2 rankable rows | 55.1Unranked · 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
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 3.1 Pro
Gemini 3.5 Flash
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationGemini 3.5 Flash
gemini-3.5-flash
Google Gemini API pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingGemini 3.5 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingGemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationGemini 3.5 Flash
Not sourced
Gemini 3.1 Pro
Gemini 3.5 Flash
Not sourced
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogGemini 3.5 Flash
Not sourced
Gemini 3.1 Pro
Reasoning
Gemini 3.5 Flash
Reasoning
Gemini 3.1 Pro
Proprietary
Gemini 3.5 Flash
Proprietary
Gemini 3.1 Pro
Proprietary
Gemini 3.5 Flash
Proprietary
Gemini 3.1 Pro
2026-02-19
Gemini 3.5 Flash
2026-05-19
Gemini 3.1 Pro has the higher public point estimate, 65.09 versus 63.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Gemini 3.5 Flash has the higher public coding point estimate, 52.3 to 42.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 3.5 Flash has the higher public agentic tasks point estimate, 50.7 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.006 on Gemini 3.5 Flash; repository review costs $0.136 and $0.102; the cache-heavy agent loop costs $0.2 and $0.15. 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.
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
τ²-bench results
Not directly comparable
Gert Labs
Shared sourceGemini 3.5 Flash leads this result
ResearchClawBench
Shared sourceGemini 3.5 Flash leads this result
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash leads this result
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
LiveCodeBench Pro
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Shared sourceGemini 3.5 Flash leads this result
LiveCodeBench (Vals)
Gemini 3.1 Pro leads this result
SWE-bench (Vals)
Tie
PostTrainBench v1.1
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Not directly comparable
cursorBench31
Not directly comparable
CursorBench 3.2
Not directly comparable
ARC-AGI-2
Gemini 3.1 Pro leads this result
ARC-AGI-3
Not directly comparable
MRCRv2
Not directly comparable
MRCR 1M
Not directly comparable
MMMU-Pro
Gemini 3.1 Pro leads this result
CharXiv
Gemini 3.5 Flash leads this result
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
Not directly comparable
Blueprint-Bench 2
Not directly comparable
GPQA-D
Gemini 3.1 Pro leads this result
HLE w/o tools
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro leads this result
MMLU-Pro (Vals)
Gemini 3.1 Pro leads this result
HLE
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 3.5 Flash leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 3.1 Pro leads this result
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Last updated October 2, 2026
