Coding
Like-for-like- MiMo-V2-Omni
- 74.8
- MiMo-V2-Pro
- 78.0
- Weighted basis
- 1 vs 1 rows
- Reading
- MiMo-V2-Pro leads
Model comparison
MiMo-V2-Omni vs MiMo-V2-Pro
Updated August 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
20 confirmed releases in the last 30 daysSee provider release alerts
MiMo-V2-Pro has the higher public score estimate, 66.75 versus 62.16, 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.
Coding work
Code generation, repair, and software-engineering tasks
MiMo-V2-Pro
MiMo-V2-Pro leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
MiMo-V2-Pro
MiMo-V2-Pro has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
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
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 Shared
2 MiMo-V2-Pro only
- Shared results
- 2
- MiMo-V2-Omni only
- 0
- MiMo-V2-Pro only
- 2
- 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- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2-Omni
- Not measured
- MiMo-V2-Pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | MiMo-V2-Omni | MiMo-V2-Pro | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 74.8 | 78.0 | Like-for-like1 vs 1 rows | MiMo-V2-Pro leads |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 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 | Not measured | Not comparable0 vs 0 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.
SWE-bench Verified
Coding
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-Omni
- API rate not published
- Fits in one request
- MiMo-V2-Pro
- API rate not published
- Fits in one request
MiMo-V2-Omni 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-Omni
- API rate not published
- Fits in one request
- MiMo-V2-Pro
- API rate not published
- Fits in one request
MiMo-V2-Omni 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-Omni
- 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-Omni 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-OmniMiMo-V2-Pro
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2-Omni
262K
MiMo-V2-Pro
1M
API model ID
MiMo-V2-Omni
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-Omni
No comparable hosted API rate
MiMo-V2-Pro
No comparable hosted API rate
Documented inputs
MiMo-V2-Omni
Not sourced
MiMo-V2-Pro
Not sourced
Documented outputs
MiMo-V2-Omni
Not sourced
MiMo-V2-Pro
Not sourced
Provider availability
MiMo-V2-Omni
Not sourced
MiMo-V2-Pro
Not sourced
Reasoning profile
MiMo-V2-Omni
Reasoning
MiMo-V2-Pro
Reasoning
Weight access
MiMo-V2-Omni
Proprietary
MiMo-V2-Pro
Proprietary
License
MiMo-V2-Omni
Proprietary
MiMo-V2-Pro
Proprietary
Release date
MiMo-V2-Omni
2026-03-18
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, 66.75 versus 62.16, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- MiMo-V2-Pro has the larger documented window (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 evidence4 rows
Agentic
Claw-Eval
Shared sourceMiMo-V2-Omni45.2%MiMo-V2-Pro57.8%MiMo-V2-Pro leads this result
Gert Labs
MiMo-V2-Omni—MiMo-V2-Pro36.68%SourceNot directly comparable
ResearchClawBench
MiMo-V2-Omni—MiMo-V2-Pro15.3%SourceNot directly comparable
Coding
Frequently asked questions
Which is better, MiMo-V2-Omni or MiMo-V2-Pro?
MiMo-V2-Pro has the higher public score estimate, 66.75 versus 62.16, 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-Omni or MiMo-V2-Pro?
MiMo-V2-Pro leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, MiMo-V2-Omni or MiMo-V2-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, MiMo-V2-Omni 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-Omni or MiMo-V2-Pro?
MiMo-V2-Pro has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated August 2, 2026
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