Updated October 19, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 10 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-Pro
MiMo-V2.5-Pro has the higher public coding point estimate, 43.2 to 34.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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-Pro 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.
Terminal-Bench 2.0Agentic
Normalized gap 2.6MMLU-Pro (Vals)Knowledge
Normalized gap 1.7SWE-bench ProCoding
Normalized gap 1.1LiveCodeBench (Vals)Coding
Normalized gap 0.1Each 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 | MiMo-V2.5 | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Coding | 34.8Supported · #79/151 | 43.2Supported · #58/151 | Like-for-likeBenchAlign v5.8 lane · 4 vs 4 public rows | MiMo-V2.5-Pro leads · intervals overlap |
| Agentic | 33.3Estimated · #88/130 | 37.8Supported · #74/130 | Directional onlyBenchAlign v5.8 lane · 6 vs 6 public rows | Directional only |
| Reasoning | Not ranked | 79.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 72.2#32/55 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 52.3Supported · #73/186 | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 93.8#7/148 | 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
MiMo-V2.5 has no comparable published API token rate. MiMo-V2.5-Pro has no comparable published API token rate.
50K fresh input + 3K output tokens
MiMo-V2.5 has no comparable published API token rate. MiMo-V2.5-Pro has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.5 has no comparable published API token rate. MiMo-V2.5-Pro 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.
MiMo-V2.5
1M
MiMo-V2.5-Pro
1M
MiMo-V2.5
Not sourced
MiMo-V2.5-Pro
Not sourced
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-V2.5-Pro
No comparable hosted API rate
MiMo-V2.5
Not sourced
MiMo-V2.5-Pro
Not sourced
MiMo-V2.5
Not sourced
MiMo-V2.5-Pro
Not sourced
MiMo-V2.5
Not sourced
MiMo-V2.5-Pro
Not sourced
MiMo-V2.5
Reasoning
MiMo-V2.5-Pro
Reasoning
MiMo-V2.5
Proprietary
MiMo-V2.5-Pro
Proprietary
MiMo-V2.5
Proprietary
MiMo-V2.5-Pro
Proprietary
MiMo-V2.5
2026-04-22
MiMo-V2.5-Pro
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-Pro has the higher public coding point estimate, 43.2 to 34.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
MiMo-V2.5-Pro scores higher for agentic tasks on the public lane, 37.8 to 33.3. 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Claw-Eval
Shared sourceMiMo-V2.5-Pro leads this result
MM-ClawBench
Not directly comparable
Terminal-Bench 2.0
MiMo-V2.5-Pro leads this result
Gert Labs
Shared sourceMiMo-V2.5-Pro leads this result
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.5 leads this result
τ³-bench results
Not directly comparable
MazeBench
Not directly comparable
SWE-bench Pro
MiMo-V2.5-Pro leads this result
Terminal-Bench 2.0
MiMo-V2.5-Pro leads this result
LiveCodeBench (Vals)
MiMo-V2.5 leads this result
SWE-bench (Vals)
MiMo-V2.5-Pro leads this result
GPQA Diamond (Vals)
MiMo-V2.5-Pro leads this result
MMLU-Pro (Vals)
MiMo-V2.5-Pro leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
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Last updated October 19, 2026
