Coding
Directional only- MiMo-V2.5
- 54.8
- Estimated · #45/183
- MiMo-V2-Pro
- 57.0
- Estimated · #38/183
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Directional only
MiMo-V2.5 vs MiMo-V2-Pro
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MiMo-V2-Pro has the higher public score estimate, 64.9 versus 60.72, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 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 and MiMo-V2-Pro are 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
MiMo-V2-Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
12 MiMo-V2.5 only
3 Shared
1 MiMo-V2-Pro only
- Shared results
- 3
- MiMo-V2.5 only
- 12
- MiMo-V2-Pro only
- 1
- Like-for-like categories
- 0 / 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.
Knowledge
Directional only- MiMo-V2.5
- 53.8
- Estimated · #65/181
- MiMo-V2-Pro
- 55.2
- Estimated · #61/181
- Basis
- BenchAlign lane · 2 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- MiMo-V2.5
- 51.1
- Estimated · #58/151
- MiMo-V2-Pro
- Not ranked
- Basis
- BenchAlign lane · 6 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiMo-V2.5
- Not ranked
- MiMo-V2-Pro
- 68.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.5
- Not ranked
- MiMo-V2-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.5
- Not ranked
- MiMo-V2-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2.5
- 58.5
- #29/48
- MiMo-V2-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2.5
- Not ranked
- MiMo-V2-Pro
- 83.7
- #48/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.5 | MiMo-V2-Pro | Basis | Reading |
|---|---|---|---|---|
| Coding | 54.8Estimated · #45/183 | 57.0Estimated · #38/183 | Directional onlyBenchAlign lane · 4 vs 1 public rows | Directional only |
| Knowledge | 53.8Estimated · #65/181 | 55.2Estimated · #61/181 | Directional onlyBenchAlign lane · 2 vs 0 public rows | Directional only |
| Agentic | 51.1Estimated · #58/151 | Not ranked | Not comparableBenchAlign lane · 6 vs 3 public rows | Not comparable |
| Reasoning | Not ranked | 68.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 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 | 58.5#29/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 83.7#48/120 | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- MiMo-V2.5
- API rate not published
- Fits in one request
- MiMo-V2-Pro
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. MiMo-V2-Pro has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiMo-V2.5
- API rate not published
- Fits in one request
- MiMo-V2-Pro
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. MiMo-V2-Pro has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiMo-V2.5
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- MiMo-V2-Pro
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5 has no comparable published API token rate. MiMo-V2-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.
SpecificationMiMo-V2.5MiMo-V2-Pro
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.5
1M
MiMo-V2-Pro
1M
API model ID
MiMo-V2.5
Not sourced
MiMo-V2-Pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
MiMo-V2.5
No comparable hosted API rate
MiMo-V2-Pro
No comparable hosted API rate
Documented inputs
MiMo-V2.5
Not sourced
MiMo-V2-Pro
Not sourced
Documented outputs
MiMo-V2.5
Not sourced
MiMo-V2-Pro
Not sourced
Provider availability
MiMo-V2.5
Not sourced
MiMo-V2-Pro
Not sourced
Reasoning profile
MiMo-V2.5
Reasoning
MiMo-V2-Pro
Reasoning
Weight access
MiMo-V2.5
Proprietary
MiMo-V2-Pro
Proprietary
License
MiMo-V2.5
Proprietary
MiMo-V2-Pro
Proprietary
Release date
MiMo-V2.5
2026-04-22
MiMo-V2-Pro
2026-03-18
If you already use one of these models
- Deployment change
- Both entries list Xiaomi as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- MiMo-V2-Pro has the higher public score estimate, 64.9 versus 60.72, 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 evidence16 rows
Agentic
Claw-Eval
Shared sourceMiMo-V2.562.3%MiMo-V2-Pro57.8%MiMo-V2.5 leads this result
MM-ClawBench
MiMo-V2.523.8%SourceMiMo-V2-Pro—Not directly comparable
Terminal-Bench 2.0
MiMo-V2.565.8%SourceMiMo-V2-Pro—Not directly comparable
Gert Labs
Shared sourceMiMo-V2.546.89%MiMo-V2-Pro36.68%MiMo-V2.5 leads this result
ResearchClawBench
Shared sourceMiMo-V2.516.9%MiMo-V2-Pro15.3%MiMo-V2.5 leads this result
Terminal-Bench 2.1 (Vals)
MiMo-V2.560.7%SourceMiMo-V2-Pro—Not directly comparable
Coding
SWE-bench Pro
MiMo-V2.556.1%SourceMiMo-V2-Pro—Not directly comparable
Terminal-Bench 2.0
MiMo-V2.565.8%SourceMiMo-V2-Pro—Not directly comparable
LiveCodeBench (Vals)
MiMo-V2.581.5%SourceMiMo-V2-Pro—Not directly comparable
SWE-bench (Vals)
MiMo-V2.571.0%SourceMiMo-V2-Pro—Not directly comparable
SWE-bench Verified
MiMo-V2.5—MiMo-V2-Pro78%SourceNot directly comparable
Knowledge
Multimodal
Frequently asked questions
Which is better, MiMo-V2.5 or MiMo-V2-Pro?
MiMo-V2-Pro has the higher public score estimate, 64.9 versus 60.72, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, MiMo-V2.5 or MiMo-V2-Pro?
MiMo-V2-Pro scores higher for coding on the public lane, 57 to 54.8. MiMo-V2.5 and MiMo-V2-Pro are 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, MiMo-V2.5 or MiMo-V2-Pro?
MiMo-V2-Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MiMo-V2.5 or MiMo-V2-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, MiMo-V2.5 or MiMo-V2-Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
Watch MiMo-V2.5 vs MiMo-V2-Pro
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.