Coding work
Code generation, repair, and software-engineering tasks
Qwen3.8-27B
Qwen3.8-27B leads on the public coding lane, 48.7 to 37.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
MiMo-V2.5 vs Qwen3.8-27B
Updated September 28, 2026. We do not rank this pair: at least one has no public score. 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
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 7
- MiMo-V2.5 only
- 8
- Qwen3.8-27B only
- 26
- Like-for-like categories
- 1 / 8
Estimated: Qwen3.8-27BHow the comparison works
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.
Long documents
Prompts that approach the documented context limit
MiMo-V2.5
MiMo-V2.5 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
MiMo-V2.5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-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-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
37.7MiMo-V2.548.7Qwen3.8-27B
Like-for-like · BenchAlign v5.7
Qwen3.8-27B leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 MiMo-V2.5 only
7 Shared
26 Qwen3.8-27B only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
CharXivMultimodal
Normalized gap 9.2MiMo-V2.5:81%Qwen3.8-27B:90.2%SWE-bench ProCoding
Normalized gap 5.6MiMo-V2.5:56.1%Qwen3.8-27B:61.7%LiveCodeBench (Vals)Coding
Normalized gap 2.5MiMo-V2.5:81.5%Qwen3.8-27B:84.0%MMLU-Pro (Vals)Knowledge
Normalized gap 1.4MiMo-V2.5:82.9%Qwen3.8-27B:84.3%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.
Coding
Like-for-like- MiMo-V2.5
- 37.7
- Supported · #69/142
- Qwen3.8-27B
- 48.7
- Supported · #45/142
- Basis
- BenchAlign v5.7 lane · 4 vs 8 public rows
- Reading
- Qwen3.8-27B leads · intervals overlap
Agentic
Directional only- MiMo-V2.5
- 31.1
- Estimated · #77/117
- Qwen3.8-27B
- 61.2
- Supported · #18/117
- Basis
- BenchAlign v5.7 lane · 6 vs 8 public rows
- Reading
- Directional only
Multimodal
Directional only- MiMo-V2.5
- 60.1
- #31/50
- Qwen3.8-27B
- 80.9
- #11/50
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- MiMo-V2.5
- Not ranked
- Qwen3.8-27B
- 78.7
- #8/27
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiMo-V2.5
- Not ranked
- Qwen3.8-27B
- 49.2
- Supported · #63/168
- Basis
- BenchAlign v5.7 lane · 2 vs 6 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.5
- Not ranked
- Qwen3.8-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- MiMo-V2.5
- Not ranked
- Qwen3.8-27B
- 83.2
- #45/124
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.5
- Not ranked
- Qwen3.8-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.5 | Qwen3.8-27B | Basis | Reading |
|---|---|---|---|---|
| Coding | 37.7Supported · #69/142 | 48.7Supported · #45/142 | Like-for-likeBenchAlign v5.7 lane · 4 vs 8 public rows | Qwen3.8-27B leads · intervals overlap |
| Agentic | 31.1Estimated · #77/117 | 61.2Supported · #18/117 | Directional onlyBenchAlign v5.7 lane · 6 vs 8 public rows | Directional only |
| Multimodal | 60.1#31/50 | 80.9#11/50 | Directional onlyProvisional lane · 2 vs 1 weighted rows | Directional only |
| Reasoning | Not ranked | 78.7#8/27 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 49.2Supported · #63/168 | Not comparableBenchAlign v5.7 lane · 2 vs 6 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 83.2#45/124 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. Qwen3.8-27B 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
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
MiMo-V2.5 has no comparable published API token rate. Qwen3.8-27B 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
- Qwen3.8-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5 has no comparable published API token rate. Qwen3.8-27B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMiMo-V2.5Qwen3.8-27B
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.5
1M
Qwen3.8-27B
API model ID
MiMo-V2.5
Not sourced
Qwen3.8-27B
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
Qwen3.8-27B
No comparable hosted API rate
Qwen3.8-27B model cardDocumented inputs
MiMo-V2.5
Not sourced
Qwen3.8-27B
Not sourced
Documented outputs
MiMo-V2.5
Not sourced
Qwen3.8-27B
Not sourced
Provider availability
MiMo-V2.5
Not sourced
Qwen3.8-27B
Not sourced
Reasoning profile
MiMo-V2.5
Reasoning
Qwen3.8-27B
Reasoning
Weight access
MiMo-V2.5
Proprietary
Qwen3.8-27B
Open Weight
License
MiMo-V2.5
Proprietary
Qwen3.8-27B
Open Weight
Release date
MiMo-V2.5
2026-04-22
Qwen3.8-27B
2026-08-05
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- MiMo-V2.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiMo-V2.5 or Qwen3.8-27B?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, MiMo-V2.5 or Qwen3.8-27B?
Qwen3.8-27B leads the public coding lane, 48.7 to 37.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, MiMo-V2.5 or Qwen3.8-27B?
Qwen3.8-27B scores higher for agentic tasks on the public lane, 61.2 to 31.1. MiMo-V2.5 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, MiMo-V2.5 or Qwen3.8-27B?
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-V2.5 or Qwen3.8-27B?
MiMo-V2.5 has the larger documented context window: 1M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Claw-Eval
MiMo-V2.562.3%SourceQwen3.8-27B—Not directly comparable
MM-ClawBench
MiMo-V2.523.8%SourceQwen3.8-27B—Not directly comparable
Terminal-Bench 2.0
MiMo-V2.565.8%SourceQwen3.8-27B—Not directly comparable
Gert Labs
MiMo-V2.546.89%SourceQwen3.8-27B—Not directly comparable
ResearchClawBench
MiMo-V2.516.9%SourceQwen3.8-27B—Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.560.7%SourceQwen3.8-27B58.4%SourceMiMo-V2.5 leads this result
Terminal-Bench 2.1
MiMo-V2.5—Qwen3.8-27B73.0%SourceNot directly comparable
CoWorkBench
MiMo-V2.5—Qwen3.8-27B70.7%SourceNot directly comparable
JobBench
MiMo-V2.5—Qwen3.8-27B33.4%SourceNot directly comparable
Agents' Last Exam
MiMo-V2.5—Qwen3.8-27B42.9%SourceNot directly comparable
OSWorld-Verified
MiMo-V2.5—Qwen3.8-27B84.3%SourceNot directly comparable
WebArena-Verified
MiMo-V2.5—Qwen3.8-27B64.8%SourceNot directly comparable
AndroidWorld
MiMo-V2.5—Qwen3.8-27B81.9%SourceNot directly comparable
Coding
SWE-bench Pro
MiMo-V2.556.1%SourceQwen3.8-27B61.7%SourceQwen3.8-27B leads this result
Terminal-Bench 2.0
MiMo-V2.565.8%SourceQwen3.8-27B—Not directly comparable
LiveCodeBench (Vals)
MiMo-V2.581.5%SourceQwen3.8-27B84.0%SourceQwen3.8-27B leads this result
SWE-bench (Vals)
MiMo-V2.571.0%SourceQwen3.8-27B86.0%SourceQwen3.8-27B leads this result
Terminal-Bench 2.1
MiMo-V2.5—Qwen3.8-27B73.0%SourceNot directly comparable
NL2Repo
MiMo-V2.5—Qwen3.8-27B42.3%SourceNot directly comparable
DeepSWE
MiMo-V2.5—Qwen3.8-27B42.2%SourceNot directly comparable
LiveCodeBench v6
MiMo-V2.5—Qwen3.8-27B90.3%SourceNot directly comparable
VulcanBench v3
MiMo-V2.5—Qwen3.8-27B82.6%SourceNot directly comparable
Multimodal
Video-MME (with subtitle)
MiMo-V2.587.7%SourceQwen3.8-27B—Not directly comparable
CharXiv
MiMo-V2.581%SourceQwen3.8-27B90.2%SourceQwen3.8-27B leads this result
MMMU-Pro
MiMo-V2.577.9%SourceQwen3.8-27B—Not directly comparable
MathVision
MiMo-V2.5—Qwen3.8-27B90.0%SourceNot directly comparable
MathVision w/ Python
MiMo-V2.5—Qwen3.8-27B94.6%SourceNot directly comparable
BabyVision
MiMo-V2.5—Qwen3.8-27B65.7%SourceNot directly comparable
BabyVision w/ Python
MiMo-V2.5—Qwen3.8-27B85.6%SourceNot directly comparable
Vision2Web
MiMo-V2.5—Qwen3.8-27B62.9%SourceNot directly comparable
CharXiv w/o tools
MiMo-V2.5—Qwen3.8-27B83.7%SourceNot directly comparable
OmniDocBench 1.5
MiMo-V2.5—Qwen3.8-27B91.1%SourceNot directly comparable
RealWorldQA
MiMo-V2.5—Qwen3.8-27B85.9%SourceNot directly comparable
ERQA
MiMo-V2.5—Qwen3.8-27B65.5%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
MiMo-V2.581.6%SourceQwen3.8-27B88.9%SourceQwen3.8-27B leads this result
MMLU-Pro (Vals)
MiMo-V2.582.9%SourceQwen3.8-27B84.3%SourceQwen3.8-27B leads this result
GPQA
MiMo-V2.5—Qwen3.8-27B89.2%SourceNot directly comparable
GPQA-D
MiMo-V2.5—Qwen3.8-27B89.2%SourceNot directly comparable
HLE
MiMo-V2.5—Qwen3.8-27B30.8%SourceNot directly comparable
HLE w/o tools
MiMo-V2.5—Qwen3.8-27B30.8%SourceNot directly comparable
Instruction following
IFBench
MiMo-V2.5—Qwen3.8-27B79.5%SourceNot directly comparable
41 public results · 7 shared
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Last updated September 28, 2026