Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
MiMo-V2.5
MiMo-V2.5 has the higher public coding point estimate, 34.8 to 30.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
MiMo-V2.5
MiMo-V2.5 has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemma 4 26B A4B is not ranked on the public lane for agentic, so no winner is named for agentic.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
MiMo-V2.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
MMMU-ProMultimodal
Normalized gap 4.1Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Gemma 4 26B A4B | MiMo-V2.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 30.9Supported · #94/151 | 34.8Supported · #79/151 | Like-for-likeBenchAlign v5.8 lane · 1 vs 4 public rows | MiMo-V2.5 leads · intervals overlap |
| Multimodal | 66.9#40/55 | 72.2#32/55 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Agentic | Not ranked | 33.3Estimated · #88/130 | Not comparableBenchAlign v5.8 lane · 0 vs 6 public rows | Not comparable |
| Reasoning | 52.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 40.0Estimated · #120/186 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 88.7#34/148 | Not ranked | 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 |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
Gemma 4 26B A4B has no comparable published API token rate. MiMo-V2.5 has no comparable published API token rate.
50K fresh input + 3K output tokens
Gemma 4 26B A4B has no comparable published API token rate. MiMo-V2.5 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Gemma 4 26B A4B has no comparable published API token rate. MiMo-V2.5 has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Gemma 4 26B A4B
MiMo-V2.5
1M
Gemma 4 26B A4B
gemma-4-26b-a4b-it
Google Gemma Gemini API guideMiMo-V2.5
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemma 4 26B A4B
No comparable hosted API rate
MiMo-V2.5
No comparable hosted API rate
Gemma 4 26B A4B
text, image
Google Gemma 4 model documentationMiMo-V2.5
Not sourced
Gemma 4 26B A4B
MiMo-V2.5
Not sourced
Gemma 4 26B A4B
Generally Available · Gemini API, Google AI Studio, open weights
Google Gemma Gemini API guideMiMo-V2.5
Not sourced
Gemma 4 26B A4B
Reasoning
MiMo-V2.5
Reasoning
Gemma 4 26B A4B
Open Weight
MiMo-V2.5
Proprietary
Gemma 4 26B A4B
Open Weight
MiMo-V2.5
Proprietary
Gemma 4 26B A4B
2026-04-02
MiMo-V2.5
2026-04-22
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.
MiMo-V2.5 has the higher public coding point estimate, 34.8 to 30.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
MiMo-V2.5 has the larger documented context window: 1M, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Not directly comparable
MM-ClawBench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
MRCRv2
Not directly comparable
GPQA
Not directly comparable
MMLU-Pro
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 19, 2026
