Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Beam 1.0 501B-A23B FP8 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
MiMo-V2.5-Pro vs Beam 1.0 501B-A23B FP8
Updated October 16, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- MiMo-V2.5-Pro only
- 11
- Beam 1.0 501B-A23B FP8 only
- 16
- Like-for-like categories
- 0 / 8
Supported: MiMo-V2.5-ProHow 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.
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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Beam 1.0 501B-A23B FP8 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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.
43.2MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP8
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
11 MiMo-V2.5-Pro only
3 Shared
16 Beam 1.0 501B-A23B FP8 only
1 category rests 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.
SWE-bench ProCoding
Normalized gap 8.7MiMo-V2.5-Pro:57.2%Beam 1.0 501B-A23B FP8:65.9%HLE w/o toolsKnowledge
Normalized gap 2.0MiMo-V2.5-Pro:34%Beam 1.0 501B-A23B FP8:36%
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 v5.8 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.
Instruction following
Directional only- MiMo-V2.5-Pro
- 93.8
- #7/148
- Beam 1.0 501B-A23B FP8
- 89.8
- #28/148
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Agentic
Not comparable- MiMo-V2.5-Pro
- 37.8
- Supported · #72/128
- Beam 1.0 501B-A23B FP8
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 6 public rows
- Reading
- Not comparable
Coding
Not comparable- MiMo-V2.5-Pro
- 43.2
- Supported · #57/150
- Beam 1.0 501B-A23B FP8
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MiMo-V2.5-Pro
- 80.6
- Unranked · 2 rankable rows
- Beam 1.0 501B-A23B FP8
- 85.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MiMo-V2.5-Pro
- Not ranked
- Beam 1.0 501B-A23B FP8
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- MiMo-V2.5-Pro
- 52.2
- Supported · #71/184
- Beam 1.0 501B-A23B FP8
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- MiMo-V2.5-Pro
- Not ranked
- Beam 1.0 501B-A23B FP8
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MiMo-V2.5-Pro
- Not ranked
- Beam 1.0 501B-A23B FP8
- 87.9
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | MiMo-V2.5-Pro | Beam 1.0 501B-A23B FP8 | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 93.8#7/148 | 89.8#28/148 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Agentic | 37.8Supported · #72/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 6 public rows | Not comparable |
| Coding | 43.2Supported · #57/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 7 public rows | Not comparable |
| Reasoning | 80.6Unranked · 2 rankable rows | 85.8Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 52.2Supported · #71/184 | 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 |
| Math | Not ranked | 87.9Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 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.
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-Pro
- API rate not published
- Fits in one request
- Beam 1.0 501B-A23B FP8
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate. Beam 1.0 501B-A23B FP8 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
- Beam 1.0 501B-A23B FP8
- API rate not published
- Fits in one request
MiMo-V2.5-Pro has no comparable published API token rate. Beam 1.0 501B-A23B FP8 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- MiMo-V2.5-Pro
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Beam 1.0 501B-A23B FP8
- API rate not published
- Fits in one request
- Cached-input rate unavailable
MiMo-V2.5-Pro has no comparable published API token rate. Beam 1.0 501B-A23B FP8 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-V2.5-ProBeam 1.0 501B-A23B FP8
Context window
Maximum documented context; output-token limits may be lower.
MiMo-V2.5-Pro
1M
Beam 1.0 501B-A23B FP8
API model ID
MiMo-V2.5-Pro
Not sourced
Beam 1.0 501B-A23B FP8
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-Pro
No comparable hosted API rate
Beam 1.0 501B-A23B FP8
No comparable hosted API rate
Reflection Hugging Face model cardDocumented inputs
MiMo-V2.5-Pro
Not sourced
Beam 1.0 501B-A23B FP8
Documented outputs
MiMo-V2.5-Pro
Not sourced
Beam 1.0 501B-A23B FP8
Provider availability
MiMo-V2.5-Pro
Not sourced
Beam 1.0 501B-A23B FP8
Open Weights · Hugging Face, self-hosted inference
Reflection Beam 1.0 FP8 model cardReasoning profile
MiMo-V2.5-Pro
Reasoning
Beam 1.0 501B-A23B FP8
Reasoning
Weight access
MiMo-V2.5-Pro
Proprietary
Beam 1.0 501B-A23B FP8
Open Weight
License
MiMo-V2.5-Pro
Proprietary
Beam 1.0 501B-A23B FP8
Open Weight
Release date
MiMo-V2.5-Pro
2026-04-22
Beam 1.0 501B-A23B FP8
2026-10-16
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
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MiMo-V2.5-Pro or Beam 1.0 501B-A23B FP8?
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-Pro or Beam 1.0 501B-A23B FP8?
Beam 1.0 501B-A23B FP8 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MiMo-V2.5-Pro or Beam 1.0 501B-A23B FP8?
Beam 1.0 501B-A23B FP8 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MiMo-V2.5-Pro or Beam 1.0 501B-A23B FP8?
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-Pro or Beam 1.0 501B-A23B FP8?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence30 rows
Agentic
Claw-Eval
MiMo-V2.5-Pro63.8%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
τ³-bench results
MiMo-V2.5-Pro72.9%SourceBeam 1.0 501B-A23B FP836.8%SourceMiMo-V2.5-Pro leads this result
Terminal-Bench 2.0
MiMo-V2.5-Pro68.4%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
Gert Labs
MiMo-V2.5-Pro62.70%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
Terminal-Bench 2.1 (Vals)
MiMo-V2.5-Pro57.3%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
MazeBench
MiMo-V2.5-Pro0%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
HLE w/ tools
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP851.1%SourceNot directly comparable
Terminal-Bench 2.1
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP878.9%SourceNot directly comparable
MCP Atlas
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP881.5%SourceNot directly comparable
BrowseComp
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP876.7%SourceNot directly comparable
DeepSearchQA
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP879.6%SourceNot directly comparable
Coding
SWE-bench Pro
MiMo-V2.5-Pro57.2%SourceBeam 1.0 501B-A23B FP865.9%SourceBeam 1.0 501B-A23B FP8 leads this result
Terminal-Bench 2.0
MiMo-V2.5-Pro68.4%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
LiveCodeBench (Vals)
MiMo-V2.5-Pro81.4%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
SWE-bench (Vals)
MiMo-V2.5-Pro74.0%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
DeepSWE
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP843.0%SourceNot directly comparable
Terminal-Bench 2.1
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP878.9%SourceNot directly comparable
SWE-Atlas Codebase QnA
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP832.2%SourceNot directly comparable
SWE Multilingual
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP878.1%SourceNot directly comparable
SWE-bench Verified
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP880.2%SourceNot directly comparable
SciCode
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP848.3%SourceNot directly comparable
Reasoning
Knowledge
HLE
MiMo-V2.5-Pro48%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
HLE w/o tools
MiMo-V2.5-Pro34%SourceBeam 1.0 501B-A23B FP836%SourceBeam 1.0 501B-A23B FP8 leads this result
GPQA Diamond (Vals)
MiMo-V2.5-Pro82.6%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
MMLU-Pro (Vals)
MiMo-V2.5-Pro84.6%SourceBeam 1.0 501B-A23B FP8—Not directly comparable
GPQA-D
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP890.3%SourceNot directly comparable
Instruction following
IFBench
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP880.7%SourceNot directly comparable
Math
AIME26
MiMo-V2.5-Pro—Beam 1.0 501B-A23B FP898.0%SourceNot directly comparable
30 public results · 3 shared
Watch MiMo-V2.5-Pro vs Beam 1.0 501B-A23B FP8
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Last updated October 16, 2026