Agentic
Directional only- GLM-5
- 50.0
- Estimated · #65/151
- MiMo-V2.5
- 51.1
- Estimated · #58/151
- Basis
- BenchAlign lane · 11 vs 6 public rows
- Reading
- Directional only
GLM-5 vs MiMo-V2.5
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5 has the higher public score estimate, 62.16 versus 60.72, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5 and MiMo-V2.5 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. MiMo-V2.5 has no comparable published API token rate.
Confidence: rate-fallback
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.
32 GLM-5 only
4 Shared
11 MiMo-V2.5 only
- Shared results
- 4
- GLM-5 only
- 32
- MiMo-V2.5 only
- 11
- Like-for-like categories
- 0 / 8
3 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.
Coding
Directional only- GLM-5
- 49.1
- Supported · #80/183
- MiMo-V2.5
- 54.8
- Estimated · #45/183
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5
- 54.9
- Estimated · #62/181
- MiMo-V2.5
- 53.8
- Estimated · #65/181
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5
- 51.6
- Unranked · 4 rankable rows
- MiMo-V2.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5
- 56.9
- #7/7
- MiMo-V2.5
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5
- 48.7
- #6/12
- MiMo-V2.5
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not ranked
- MiMo-V2.5
- 58.5
- #29/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5
- 88.3
- #31/120
- MiMo-V2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5 | MiMo-V2.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.0Estimated · #65/151 | 51.1Estimated · #58/151 | Directional onlyBenchAlign lane · 11 vs 6 public rows | Directional only |
| Coding | 49.1Supported · #80/183 | 54.8Estimated · #45/183 | Directional onlyBenchAlign lane · 6 vs 4 public rows | Directional only |
| Knowledge | 54.9Estimated · #62/181 | 53.8Estimated · #65/181 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Reasoning | 51.6Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 56.9#7/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | 48.7#6/12 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 58.5#29/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Instruction following | 88.3#31/120 | 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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
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
- GLM-5
- $0.0026
- Fits in one request
- MiMo-V2.5
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5
- $0.0596
- Fits in one request
- MiMo-V2.5
- API rate not published
- Fits in one request
MiMo-V2.5 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5
- $0.252
- Does not fit in one request
- Cached input priced at the published list-input rate
- MiMo-V2.5
- API rate not published
- Fits in one request
- Cached-input rate unavailable
GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. MiMo-V2.5 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.
SpecificationGLM-5MiMo-V2.5
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
MiMo-V2.5
1M
API model ID
GLM-5
Not sourced
MiMo-V2.5
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5
Not published
MiMo-V2.5
No comparable hosted API rate
Documented inputs
GLM-5
Not sourced
MiMo-V2.5
Not sourced
Documented outputs
GLM-5
Not sourced
MiMo-V2.5
Not sourced
Provider availability
GLM-5
Not sourced
MiMo-V2.5
Not sourced
Reasoning profile
GLM-5
Non-Reasoning
MiMo-V2.5
Reasoning
Weight access
GLM-5
Open Weight
MiMo-V2.5
Proprietary
License
GLM-5
Open Weight
MiMo-V2.5
Proprietary
Release date
GLM-5
2026-03-01
MiMo-V2.5
2026-04-22
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
- GLM-5 has the higher public score estimate, 62.16 versus 60.72, but the 90% score intervals overlap.
- 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence47 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceMiMo-V2.565.8%SourceMiMo-V2.5 leads this result
Claw-Eval
GLM-557.7%SourceMiMo-V2.562.3%SourceMiMo-V2.5 leads this result
QwenClawBench
GLM-554.1%SourceMiMo-V2.5—Not directly comparable
τ³-bench results
GLM-565.6%SourceMiMo-V2.5—Not directly comparable
DeepPlanning
GLM-514.6%SourceMiMo-V2.5—Not directly comparable
Toolathlon
GLM-538%SourceMiMo-V2.5—Not directly comparable
MCP Atlas
GLM-531.1%SourceMiMo-V2.5—Not directly comparable
MCP-Tasks
GLM-560.8%SourceMiMo-V2.5—Not directly comparable
WideResearch
GLM-569.8%SourceMiMo-V2.5—Not directly comparable
CyberGym
GLM-543.2%SourceMiMo-V2.5—Not directly comparable
Gert Labs
Shared sourceGLM-550.99%MiMo-V2.546.89%GLM-5 leads this result
MM-ClawBench
GLM-5—MiMo-V2.523.8%SourceNot directly comparable
ResearchClawBench
GLM-5—MiMo-V2.516.9%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5—MiMo-V2.560.7%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceMiMo-V2.5—Not directly comparable
SWE-bench Verified*
GLM-572.8%SourceMiMo-V2.5—Not directly comparable
SWE-bench Pro
GLM-555.1%SourceMiMo-V2.556.1%SourceMiMo-V2.5 leads this result
SWE Multilingual
GLM-573.3%SourceMiMo-V2.5—Not directly comparable
SWE-Rebench
GLM-562.8%SourceMiMo-V2.5—Not directly comparable
React Native Evals
GLM-574.8%SourceMiMo-V2.5—Not directly comparable
Terminal-Bench 2.0
GLM-5—MiMo-V2.565.8%SourceNot directly comparable
LiveCodeBench (Vals)
GLM-5—MiMo-V2.581.5%SourceNot directly comparable
SWE-bench (Vals)
GLM-5—MiMo-V2.571.0%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-586%SourceMiMo-V2.5—Not directly comparable
GPQA-D
GLM-586.0%SourceMiMo-V2.5—Not directly comparable
SuperGPQA
GLM-566.8%SourceMiMo-V2.5—Not directly comparable
MMLU-Pro
GLM-585.7%SourceMiMo-V2.5—Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%SourceMiMo-V2.5—Not directly comparable
HLE
GLM-550.4%SourceMiMo-V2.5—Not directly comparable
GPQA Diamond (Vals)
GLM-5—MiMo-V2.581.6%SourceNot directly comparable
MMLU-Pro (Vals)
GLM-5—MiMo-V2.582.9%SourceNot directly comparable
Math
AIME26
GLM-595.8%SourceMiMo-V2.5—Not directly comparable
AIME25 (Arcee)
GLM-593.3%SourceMiMo-V2.5—Not directly comparable
HMMT Feb 2025
GLM-597.5%SourceMiMo-V2.5—Not directly comparable
HMMT Nov 2025
GLM-596.9%SourceMiMo-V2.5—Not directly comparable
HMMT Feb 2026
GLM-586.4%SourceMiMo-V2.5—Not directly comparable
MMAnswerBench
GLM-582.5%SourceMiMo-V2.5—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-516.434%SourceMiMo-V2.5—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-52.100%SourceMiMo-V2.5—Not directly comparable
Multilingual
Multimodal
Instruction following
IFEval
GLM-592.6%SourceMiMo-V2.5—Not directly comparable
Frequently asked questions
Which is better, GLM-5 or MiMo-V2.5?
GLM-5 has the higher public score estimate, 62.16 versus 60.72, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-5 or MiMo-V2.5?
MiMo-V2.5 scores higher for coding on the public lane, 54.8 to 49.1. MiMo-V2.5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5 or MiMo-V2.5?
MiMo-V2.5 scores higher for agentic tasks on the public lane, 51.1 to 50. GLM-5 and MiMo-V2.5 are 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, GLM-5 or MiMo-V2.5?
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, GLM-5 or MiMo-V2.5?
MiMo-V2.5 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 4, 2026
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