Math
Like-for-like- Gemini 2.5 Flash
- 4.7
- Gemini 3.1 Pro
- 31.8
- Weighted basis
- 2 vs 2 rows
- Reading
- Gemini 3.1 Pro leads
Gemini 2.5 Flash vs Gemini 3.1 Pro
Updated September 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.1 Pro has the higher public score estimate, 56.49 versus 47.8, but the 90% score intervals overlap. Treat that as a lead, not a settled 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 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
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.
2 Shared
21 Gemini 3.1 Pro only
- Shared results
- 2
- Gemini 2.5 Flash only
- 0
- Gemini 3.1 Pro only
- 21
- Like-for-like categories
- 1 / 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.
Agentic
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- 77.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- 82.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Flash
- Not measured
- Gemini 3.1 Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 2.5 Flash | Gemini 3.1 Pro | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 4.7 | 31.8 | Like-for-like2 vs 2 rows | Gemini 3.1 Pro leads |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | 77.1 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 82.6 | Not comparable0 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.
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
- Gemini 3.1 Pro
- $0.008
- 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
- Gemini 3.1 Pro
- $0.136
- 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
- Gemini 3.1 Pro
- $0.2
- 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 FlashGemini 3.1 Pro
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Flash
Gemini 3.1 Pro
API model ID
Gemini 2.5 Flash
gemini-2.5-flash
Google Gemini API pricingGemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationCached-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 pricingGemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingDocumented inputs
Gemini 2.5 Flash
Not sourced
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationDocumented outputs
Gemini 2.5 Flash
Not sourced
Gemini 3.1 Pro
Provider availability
Gemini 2.5 Flash
Not sourced
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogReasoning profile
Gemini 2.5 Flash
Non-Reasoning
Gemini 3.1 Pro
Reasoning
Weight access
Gemini 2.5 Flash
Proprietary
Gemini 3.1 Pro
Proprietary
License
Gemini 2.5 Flash
Proprietary
Gemini 3.1 Pro
Proprietary
Release date
Gemini 2.5 Flash
2025-06-17
Gemini 3.1 Pro
2026-02-19
If you already use one of these models
- Deployment change
- Both entries list Google as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- Gemini 3.1 Pro has the higher public score estimate, 56.49 versus 47.8, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0225 vs $0.136. Cache-heavy agent loop: $0.037 vs $0.2.
- 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 evidence23 rows
Agentic
Claw-Eval
Gemini 2.5 Flash—Gemini 3.1 Pro57.8%SourceNot directly comparable
DeepSearchQA
Gemini 2.5 Flash—Gemini 3.1 Pro69.7%SourceNot directly comparable
τ²-bench results
Gemini 2.5 Flash—Gemini 3.1 Pro95.6%SourceNot directly comparable
Gert Labs
Gemini 2.5 Flash—Gemini 3.1 Pro56.87%SourceNot directly comparable
ResearchClawBench
Gemini 2.5 Flash—Gemini 3.1 Pro13.3%SourceNot directly comparable
Coding
Reasoning
Knowledge
GPQA-D
Gemini 2.5 Flash—Gemini 3.1 Pro94.3%SourceNot directly comparable
HLE w/o tools
Gemini 2.5 Flash—Gemini 3.1 Pro45.4%SourceNot directly comparable
HealthBench Hard
Gemini 2.5 Flash—Gemini 3.1 Pro20.6%SourceNot directly comparable
MedXpertQA (Text)
Gemini 2.5 Flash—Gemini 3.1 Pro71.5%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGemini 2.5 Flash4.844%Gemini 3.1 Pro36.900%Gemini 3.1 Pro leads this result
FrontierMath v2 (Tier 4)
Shared sourceGemini 2.5 Flash4.167%Gemini 3.1 Pro16.700%Gemini 3.1 Pro leads this result
Multimodal
MMMU-Pro
Gemini 2.5 Flash—Gemini 3.1 Pro83.9%SourceNot directly comparable
CharXiv
Gemini 2.5 Flash—Gemini 3.1 Pro80.2%SourceNot directly comparable
ERQA
Gemini 2.5 Flash—Gemini 3.1 Pro69.4%SourceNot directly comparable
SimpleVQA
Gemini 2.5 Flash—Gemini 3.1 Pro72.4%SourceNot directly comparable
ScreenSpot Pro
Gemini 2.5 Flash—Gemini 3.1 Pro84.4%SourceNot directly comparable
ZeroBench
Gemini 2.5 Flash—Gemini 3.1 Pro29.0%SourceNot directly comparable
MedXpertQA (MM)
Gemini 2.5 Flash—Gemini 3.1 Pro81.3%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 2.5 Flash or Gemini 3.1 Pro?
Gemini 3.1 Pro has the higher public score estimate, 56.49 versus 47.8, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 2.5 Flash or Gemini 3.1 Pro?
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 2.5 Flash or Gemini 3.1 Pro?
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 2.5 Flash or Gemini 3.1 Pro?
For the stated presets, chat costs $0.00155 on Gemini 2.5 Flash and $0.008 on Gemini 3.1 Pro; repository review costs $0.0225 and $0.136; the cache-heavy agent loop costs $0.037 and $0.2. Costs use the listed standard API rates.
Which has the larger context window, Gemini 2.5 Flash or Gemini 3.1 Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 3, 2026
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