Knowledge
Like-for-like- Gemini 3.5 Flash-Lite
- 53.0
- Supported · #71/181
- MiMo-V2.5-Pro
- 55.4
- Supported · #59/181
- Basis
- BenchAlign lane · 2 vs 4 public rows
- Reading
- MiMo-V2.5-Pro leads · intervals overlap
Gemini 3.5 Flash-Lite 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
MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.5, 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic, 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.
2 Gemini 3.5 Flash-Lite only
8 Shared
5 MiMo-V2.5-Pro only
- Shared results
- 8
- Gemini 3.5 Flash-Lite only
- 2
- MiMo-V2.5-Pro only
- 5
- Like-for-like categories
- 1 / 8
2 categories rest 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.
Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- MiMo-V2.5-Pro
- 40.8
- Supported · #118/151
- Basis
- BenchAlign lane · 3 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- Gemini 3.5 Flash-Lite
- 43.6
- Supported · #121/183
- MiMo-V2.5-Pro
- 57.1
- Estimated · #36/183
- Basis
- BenchAlign lane · 4 vs 4 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.5 Flash-Lite
- 60.8
- Unranked · 3 rankable rows
- MiMo-V2.5-Pro
- 76.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.5 Flash-Lite
- 76.1
- #16/48
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.5 Flash-Lite
- Not ranked
- MiMo-V2.5-Pro
- 93.5
- #6/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.5 Flash-Lite | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 53.0Supported · #71/181 | 55.4Supported · #59/181 | Like-for-likeBenchAlign lane · 2 vs 4 public rows | MiMo-V2.5-Pro leads · intervals overlap |
| Agentic | 43.4Estimated · #104/151 | 40.8Supported · #118/151 | Directional onlyBenchAlign lane · 3 vs 5 public rows | Directional only |
| Coding | 43.6Supported · #121/183 | 57.1Estimated · #36/183 | Directional onlyBenchAlign lane · 4 vs 4 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | 76.9Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.1#16/48 | Not ranked | Not comparableProvisional lane · 0 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.
Terminal-Bench 2.0
Agentic
Gemini 3.5 Flash-Lite: 54%MiMo-V2.5-Pro: 68.4%Normalized gap 14.4Gemini 3.5 Flash-Lite source MiMo-V2.5-Pro sourceSWE-bench Pro
Coding
Gemini 3.5 Flash-Lite: 54.2%MiMo-V2.5-Pro: 57.2%Normalized gap 3.0Gemini 3.5 Flash-Lite source MiMo-V2.5-Pro sourceLiveCodeBench (Vals)
Coding
Gemini 3.5 Flash-Lite: 79.0%MiMo-V2.5-Pro: 81.4%Normalized gap 2.4Gemini 3.5 Flash-Lite source MiMo-V2.5-Pro sourceMMLU-Pro (Vals)
Knowledge
Gemini 3.5 Flash-Lite: 85.8%MiMo-V2.5-Pro: 84.6%Normalized gap 1.2Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite
- $0.00155
- 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 3.5 Flash-Lite
- $0.0225
- 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 3.5 Flash-Lite
- $0.037
- 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 3.5 Flash-LiteMiMo-V2.5-Pro
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.5 Flash-Lite
MiMo-V2.5-Pro
1M
API model ID
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingMiMo-V2.5-Pro
No comparable hosted API rate
Documented inputs
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMiMo-V2.5-Pro
Not sourced
Documented outputs
Gemini 3.5 Flash-Lite
MiMo-V2.5-Pro
Not sourced
Provider availability
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMiMo-V2.5-Pro
Not sourced
Reasoning profile
Gemini 3.5 Flash-Lite
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
Gemini 3.5 Flash-Lite
Proprietary
MiMo-V2.5-Pro
Proprietary
License
Gemini 3.5 Flash-Lite
Proprietary
MiMo-V2.5-Pro
Proprietary
Release date
Gemini 3.5 Flash-Lite
2026-07-21
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
- MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.5, 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 evidence15 rows
Agentic
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%SourceMiMo-V2.5-Pro68.4%SourceMiMo-V2.5-Pro leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite50.2%SourceMiMo-V2.5-Pro57.3%SourceMiMo-V2.5-Pro leads this result
Claw-Eval
Gemini 3.5 Flash-Lite—MiMo-V2.5-Pro63.8%SourceNot directly comparable
τ³-bench results
Gemini 3.5 Flash-Lite—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Gert Labs
Gemini 3.5 Flash-Lite—MiMo-V2.5-Pro62.70%SourceNot directly comparable
Coding
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%SourceMiMo-V2.5-Pro68.4%SourceMiMo-V2.5-Pro leads this result
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%SourceMiMo-V2.5-Pro57.2%SourceMiMo-V2.5-Pro leads this result
LiveCodeBench (Vals)
Gemini 3.5 Flash-Lite79.0%SourceMiMo-V2.5-Pro81.4%SourceMiMo-V2.5-Pro leads this result
SWE-bench (Vals)
Gemini 3.5 Flash-Lite75.0%SourceMiMo-V2.5-Pro74.0%SourceGemini 3.5 Flash-Lite leads this result
Reasoning
MRCRv2
Gemini 3.5 Flash-Lite72.2%SourceMiMo-V2.5-Pro—Not directly comparable
Knowledge
GPQA Diamond (Vals)
Gemini 3.5 Flash-Lite83.8%SourceMiMo-V2.5-Pro82.6%SourceGemini 3.5 Flash-Lite leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite85.8%SourceMiMo-V2.5-Pro84.6%SourceGemini 3.5 Flash-Lite leads this result
HLE
Gemini 3.5 Flash-Lite—MiMo-V2.5-Pro48%SourceNot directly comparable
HLE w/o tools
Gemini 3.5 Flash-Lite—MiMo-V2.5-Pro34%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.5 Flash-Lite or MiMo-V2.5-Pro?
MiMo-V2.5-Pro has the higher public score estimate, 65.41 versus 60.5, 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 3.5 Flash-Lite or MiMo-V2.5-Pro?
MiMo-V2.5-Pro scores higher for coding on the public lane, 57.1 to 43.6. 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 3.5 Flash-Lite or MiMo-V2.5-Pro?
Gemini 3.5 Flash-Lite scores higher for agentic tasks on the public lane, 43.4 to 40.8. Gemini 3.5 Flash-Lite is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 3.5 Flash-Lite 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 3.5 Flash-Lite or MiMo-V2.5-Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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