Agentic
Like-for-like- Gemini 3.1 Pro
- 40.1
- Supported · #120/151
- MiMo-V2.5-Pro
- 40.8
- Supported · #118/151
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- MiMo-V2.5-Pro leads · intervals overlap
Gemini 3.1 Pro vs MiMo-V2.5-Pro
Updated September 4, 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, 69.82 versus 65.41, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Agentic work
Tool use, computer use, and multi-step task completion
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads on the public agentic lane, 40.8 to 40.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.5-Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 Gemini 3.1 Pro only
8 Shared
5 MiMo-V2.5-Pro only
- Shared results
- 8
- Gemini 3.1 Pro only
- 20
- MiMo-V2.5-Pro only
- 5
- Like-for-like categories
- 2 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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.
Knowledge
Like-for-like- Gemini 3.1 Pro
- 65.6
- Supported · #24/181
- MiMo-V2.5-Pro
- 55.5
- Supported · #58/181
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Gemini 3.1 Pro leads · intervals overlap
Coding
Directional only- Gemini 3.1 Pro
- 46.3
- Supported · #99/183
- MiMo-V2.5-Pro
- 57.1
- Estimated · #36/183
- Basis
- BenchAlign lane · 5 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.1 Pro
- 50.7
- Unranked · 2 rankable rows
- MiMo-V2.5-Pro
- 76.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.1 Pro
- 54.3
- Unranked · 2 rankable rows
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.1 Pro
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.1 Pro
- 79.2
- #12/48
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.1 Pro
- Not ranked
- MiMo-V2.5-Pro
- 93.5
- #6/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.1 Pro | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.1Supported · #120/151 | 40.8Supported · #118/151 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | MiMo-V2.5-Pro leads · intervals overlap |
| Knowledge | 65.6Supported · #24/181 | 55.5Supported · #58/181 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Coding | 46.3Supported · #99/183 | 57.1Estimated · #36/183 | Directional onlyBenchAlign lane · 5 vs 4 public rows | Directional only |
| Reasoning | 50.7Unranked · 2 rankable rows | 76.9Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 54.3Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 79.2#12/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.5#6/120 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
HLE w/o tools
Knowledge
Gemini 3.1 Pro: 45.4%MiMo-V2.5-Pro: 34%Normalized gap 11.4Gemini 3.1 Pro source MiMo-V2.5-Pro sourceLiveCodeBench (Vals)
Coding
Gemini 3.1 Pro: 88.5%MiMo-V2.5-Pro: 81.4%Normalized gap 7.1Gemini 3.1 Pro source MiMo-V2.5-Pro sourceMMLU-Pro (Vals)
Knowledge
Gemini 3.1 Pro: 91.0%MiMo-V2.5-Pro: 84.6%Normalized gap 6.4Gemini 3.1 Pro source MiMo-V2.5-Pro source
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 3.1 Pro
- $0.008
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.1 Pro
- $0.136
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.1 Pro
- $0.2
- Fits in one request
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5-Pro has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.1 ProMiMo-V2.5-Pro
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
MiMo-V2.5-Pro
1M
API model ID
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationMiMo-V2.5-Pro
Not sourced
Cached-input rate
A 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 pricingMiMo-V2.5-Pro
No comparable hosted API rate
Documented inputs
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationMiMo-V2.5-Pro
Not sourced
Documented outputs
Gemini 3.1 Pro
MiMo-V2.5-Pro
Not sourced
Provider availability
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogMiMo-V2.5-Pro
Not sourced
Reasoning profile
Gemini 3.1 Pro
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
Gemini 3.1 Pro
Proprietary
MiMo-V2.5-Pro
Proprietary
License
Gemini 3.1 Pro
Proprietary
MiMo-V2.5-Pro
Proprietary
Release date
Gemini 3.1 Pro
2026-02-19
MiMo-V2.5-Pro
2026-04-22
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
- Gemini 3.1 Pro has the higher public score estimate, 69.82 versus 65.41, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- 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 evidence33 rows
Agentic
Claw-Eval
Shared sourceGemini 3.1 Pro57.8%MiMo-V2.5-Pro63.8%MiMo-V2.5-Pro leads this result
DeepSearchQA
Gemini 3.1 Pro69.7%SourceMiMo-V2.5-Pro—Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%SourceMiMo-V2.5-Pro—Not directly comparable
Gert Labs
Shared sourceGemini 3.1 Pro56.87%MiMo-V2.5-Pro62.70%MiMo-V2.5-Pro leads this result
ResearchClawBench
Gemini 3.1 Pro13.3%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%SourceMiMo-V2.5-Pro57.3%SourceGemini 3.1 Pro leads this result
τ³-bench results
Gemini 3.1 Pro—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—MiMo-V2.5-Pro68.4%SourceNot directly comparable
Coding
LiveCodeBench Pro
Gemini 3.1 Pro82.9%SourceMiMo-V2.5-Pro—Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%SourceMiMo-V2.5-Pro—Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%SourceMiMo-V2.5-Pro—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%SourceMiMo-V2.5-Pro81.4%SourceGemini 3.1 Pro leads this result
SWE-bench (Vals)
Gemini 3.1 Pro78.8%SourceMiMo-V2.5-Pro74.0%SourceGemini 3.1 Pro leads this result
SWE-bench Pro
Gemini 3.1 Pro—MiMo-V2.5-Pro57.2%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3.1 Pro—MiMo-V2.5-Pro68.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA-D
Gemini 3.1 Pro94.3%SourceMiMo-V2.5-Pro—Not directly comparable
HLE w/o tools
Gemini 3.1 Pro45.4%SourceMiMo-V2.5-Pro34%SourceGemini 3.1 Pro leads this result
HealthBench Hard
Gemini 3.1 Pro20.6%SourceMiMo-V2.5-Pro—Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%SourceMiMo-V2.5-Pro82.6%SourceGemini 3.1 Pro leads this result
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%SourceMiMo-V2.5-Pro84.6%SourceGemini 3.1 Pro leads this result
HLE
Gemini 3.1 Pro—MiMo-V2.5-Pro48%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
Gemini 3.1 Pro83.9%SourceMiMo-V2.5-Pro—Not directly comparable
CharXiv
Gemini 3.1 Pro80.2%SourceMiMo-V2.5-Pro—Not directly comparable
ERQA
Gemini 3.1 Pro69.4%SourceMiMo-V2.5-Pro—Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%SourceMiMo-V2.5-Pro—Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%SourceMiMo-V2.5-Pro—Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%SourceMiMo-V2.5-Pro—Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%SourceMiMo-V2.5-Pro—Not directly comparable
Frequently asked questions
Which is better, Gemini 3.1 Pro or MiMo-V2.5-Pro?
Gemini 3.1 Pro has the higher public score estimate, 69.82 versus 65.41, 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 3.1 Pro or MiMo-V2.5-Pro?
MiMo-V2.5-Pro scores higher for coding on the public lane, 57.1 to 46.3. MiMo-V2.5-Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 3.1 Pro or MiMo-V2.5-Pro?
MiMo-V2.5-Pro leads the public agentic tasks lane, 40.8 to 40.1, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Gemini 3.1 Pro or MiMo-V2.5-Pro?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Gemini 3.1 Pro or MiMo-V2.5-Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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