Knowledge
Directional only- MiMo-V2-Omni
- 49.4
- Estimated · #86/184
- Ternary Bonsai 2 27B
- 50.6
- Estimated · #78/184
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
MiMo-V2-Omni vs Ternary Bonsai 2 27B
Decision reading
MiMo-V2-Omni has the higher public score estimate, 57.53 versus 50.78, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. Rank says MiMo-V2-Omni is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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-Omni is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2-Omni 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.
1 MiMo-V2-Omni only
1 Shared
20 Ternary Bonsai 2 27B only
- Shared results
- 1
- MiMo-V2-Omni only
- 1
- Ternary Bonsai 2 27B only
- 20
- 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.
Instruction following
Directional only- MiMo-V2-Omni
- 62.6
- #72/124
- Ternary Bonsai 2 27B
- 71.0
- #64/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- MiMo-V2-Omni
- Not ranked
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #59/154
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- MiMo-V2-Omni
- Not ranked
- Ternary Bonsai 2 27B
- 49.9
- Estimated · #64/154
- Basis
- BenchAlign lane · 1 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiMo-V2-Omni
- 72.6
- Unranked · 2 rankable rows
- Ternary Bonsai 2 27B
- 73.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2-Omni
- Not ranked
- Ternary Bonsai 2 27B
- 76.8
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2-Omni
- Not ranked
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2-Omni
- 63.1
- Unranked · 1 rankable row
- Ternary Bonsai 2 27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2-Omni | Ternary Bonsai 2 27B | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 49.4Estimated · #86/184 | 50.6Estimated · #78/184 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Instruction following | 62.6#72/124 | 71.0#64/124 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | Not ranked | 49.9Estimated · #59/154 | Not comparableBenchAlign lane · 1 vs 3 public rows | Not comparable |
| Coding | Not ranked | 49.9Estimated · #64/154 | Not comparableBenchAlign lane · 1 vs 4 public rows | Not comparable |
| Reasoning | 72.6Unranked · 2 rankable rows | 73.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 76.8Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 63.1Unranked · 1 rankable row | Not ranked | 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.
SWE-bench Verified
Coding
MiMo-V2-Omni: 74.8%Ternary Bonsai 2 27B: 60.8%Normalized gap 14.0MiMo-V2-Omni source Ternary Bonsai 2 27B 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
- MiMo-V2-Omni
- API rate not published
- Fits in one request
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
MiMo-V2-Omni has no comparable published API token rate. Ternary Bonsai 2 27B 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
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
MiMo-V2-Omni has no comparable published API token rate. Ternary Bonsai 2 27B 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
- Ternary Bonsai 2 27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MiMo-V2-Omni has no comparable published API token rate. Ternary Bonsai 2 27B 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-OmniTernary Bonsai 2 27B
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2-Omni
262K
Ternary Bonsai 2 27B
API model ID
MiMo-V2-Omni
Not sourced
Ternary Bonsai 2 27B
prism-ml/Ternary-Bonsai-2-27B-gguf
PrismML Bonsai 2 collectionCached-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
Ternary Bonsai 2 27B
No comparable hosted API rate
PrismML Bonsai 2 collectionDocumented inputs
MiMo-V2-Omni
Not sourced
Ternary Bonsai 2 27B
Not sourced
Documented outputs
MiMo-V2-Omni
Not sourced
Ternary Bonsai 2 27B
Not sourced
Provider availability
MiMo-V2-Omni
Not sourced
Ternary Bonsai 2 27B
Not sourced
Reasoning profile
MiMo-V2-Omni
Reasoning
Ternary Bonsai 2 27B
Reasoning
Weight access
MiMo-V2-Omni
Proprietary
Ternary Bonsai 2 27B
Open Weight
License
MiMo-V2-Omni
Proprietary
Ternary Bonsai 2 27B
Open Weight
Release date
MiMo-V2-Omni
2026-03-18
Ternary Bonsai 2 27B
2026-09-17
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-Omni has the higher public score estimate, 57.53 versus 50.78, 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 262K.
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 evidence22 rows
Agentic
Claw-Eval
MiMo-V2-Omni45.2%SourceTernary Bonsai 2 27B—Not directly comparable
τ²-bench results
MiMo-V2-Omni—Ternary Bonsai 2 27B80.2%SourceNot directly comparable
BFCL v3
MiMo-V2-Omni—Ternary Bonsai 2 27B74.9%SourceNot directly comparable
Terminal-Bench 2.1
MiMo-V2-Omni—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Coding
SWE-bench Verified
MiMo-V2-Omni74.8%SourceTernary Bonsai 2 27B60.8%SourceMiMo-V2-Omni leads this result
LiveCodeBench v6
MiMo-V2-Omni—Ternary Bonsai 2 27B90.1%SourceNot directly comparable
BigCodeBench
MiMo-V2-Omni—Ternary Bonsai 2 27B58.1%SourceNot directly comparable
Terminal-Bench 2.1
MiMo-V2-Omni—Ternary Bonsai 2 27B52.8%SourceNot directly comparable
Knowledge
Math
Multimodal
CharXiv (overall)
MiMo-V2-Omni—Ternary Bonsai 2 27B80.0%SourceNot directly comparable
A-OKVQA
MiMo-V2-Omni—Ternary Bonsai 2 27B86.8%SourceNot directly comparable
OmniDocBench 1.6
MiMo-V2-Omni—Ternary Bonsai 2 27B89.1%SourceNot directly comparable
RealWorldQA
MiMo-V2-Omni—Ternary Bonsai 2 27B80.1%SourceNot directly comparable
OCRBench V2
MiMo-V2-Omni—Ternary Bonsai 2 27B56.9%SourceNot directly comparable
Instruction following
Questions
Which is better, MiMo-V2-Omni or Ternary Bonsai 2 27B?
MiMo-V2-Omni has the higher public score estimate, 57.53 versus 50.78, 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 Ternary Bonsai 2 27B?
MiMo-V2-Omni is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MiMo-V2-Omni or Ternary Bonsai 2 27B?
MiMo-V2-Omni is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MiMo-V2-Omni or Ternary Bonsai 2 27B?
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 Ternary Bonsai 2 27B?
Both models list the same context window, 262K.
Related comparisons
Last updated September 18, 2026
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