Agentic
Like-for-like- GLM-5.2
- 81.0
- MiMo-V2.5
- 65.8
- Weighted basis
- 1 vs 1 rows
- Reading
- GLM-5.2 leads
Model comparison
GLM-5.2 vs MiMo-V2.5
Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
21 confirmed releases in the last 30 daystrack changes
GLM-5.2 has the higher public score estimate, 62.94 versus 57.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Coding work
Code generation, repair, and software-engineering tasks
GLM-5.2
GLM-5.2 leads on the same 1 weighted benchmark row.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the same 1 weighted benchmark row.
Confidence: limited
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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
A complete comparable API-rate estimate is not available for both models.
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.
14 GLM-5.2 only
4 Shared
6 MiMo-V2.5 only
- Shared results
- 4
- GLM-5.2 only
- 14
- MiMo-V2.5 only
- 6
- Like-for-like categories
- 2 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Like-for-like- GLM-5.2
- 62.1
- MiMo-V2.5
- 56.1
- Weighted basis
- 1 vs 1 rows
- Reading
- GLM-5.2 leads
Reasoning
Not comparable- GLM-5.2
- Not measured
- MiMo-V2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.2
- 59.6
- MiMo-V2.5
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 95.9
- MiMo-V2.5
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- MiMo-V2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- MiMo-V2.5
- 79.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- MiMo-V2.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.2 | MiMo-V2.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 81.0 | 65.8 | Like-for-like1 vs 1 rows | GLM-5.2 leads |
| Coding | 62.1 | 56.1 | Like-for-like1 vs 1 rows | GLM-5.2 leads |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 59.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | 95.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 79.0 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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-5.2
- $0.0036
- 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-5.2
- $0.0832
- 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-5.2
- $0.352
- Fits 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-5.2 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-5.2MiMo-V2.5
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
MiMo-V2.5
1M
API model ID
GLM-5.2
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-5.2
Not published
MiMo-V2.5
No comparable hosted API rate
Documented inputs
GLM-5.2
Not sourced
MiMo-V2.5
Not sourced
Documented outputs
GLM-5.2
Not sourced
MiMo-V2.5
Not sourced
Provider availability
GLM-5.2
Not sourced
MiMo-V2.5
Not sourced
Reasoning profile
GLM-5.2
Reasoning
MiMo-V2.5
Reasoning
Weight access
GLM-5.2
Open Weight
MiMo-V2.5
Proprietary
License
GLM-5.2
Open Weight
MiMo-V2.5
Proprietary
Release date
GLM-5.2
2026-06-16
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-5.2 has the higher public score estimate, 62.94 versus 57.9, but the 90% score intervals overlap.
- 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence24 rows
Agentic
Terminal-Bench 2.0
GLM-5.281%SourceMiMo-V2.565.8%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceMiMo-V2.5—Not directly comparable
Toolathlon
GLM-5.248.2%SourceMiMo-V2.5—Not directly comparable
ResearchClawBench
Shared sourceGLM-5.220.7%MiMo-V2.516.9%GLM-5.2 leads this result
Claw-Eval
GLM-5.2—MiMo-V2.562.3%SourceNot directly comparable
MM-ClawBench
GLM-5.2—MiMo-V2.523.8%SourceNot directly comparable
Gert Labs
GLM-5.2—MiMo-V2.546.89%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceMiMo-V2.556.1%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceMiMo-V2.5—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceMiMo-V2.565.8%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceMiMo-V2.5—Not directly comparable
cursorBench32
GLM-5.255.0%SourceMiMo-V2.5—Not directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceMiMo-V2.5—Not directly comparable
Knowledge
Math
Multimodal
Frequently asked questions
Which is better, GLM-5.2 or MiMo-V2.5?
GLM-5.2 has the higher public score estimate, 62.94 versus 57.9, 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-5.2 or MiMo-V2.5?
GLM-5.2 leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, GLM-5.2 or MiMo-V2.5?
GLM-5.2 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, GLM-5.2 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-5.2 or MiMo-V2.5?
Both models list the same context window, 1M.
Related comparisons
Last updated July 31, 2026
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