Agentic
Like-for-like- GLM-5.2
- 58.5
- Supported · #29/153
- MiMo-V2.5-Pro
- 39.8
- Supported · #119/153
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- GLM-5.2 leads
GLM-5.2 vs MiMo-V2.5-Pro
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5.2 has the higher public score estimate, 68.19 versus 62.92, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
10 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.
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 39.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.5-Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
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.
15 GLM-5.2 only
10 Shared
3 MiMo-V2.5-Pro only
- Shared results
- 10
- GLM-5.2 only
- 15
- MiMo-V2.5-Pro only
- 3
- Like-for-like categories
- 2 / 8
2 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.
Knowledge
Like-for-like- GLM-5.2
- 60.7
- Supported · #35/183
- MiMo-V2.5-Pro
- 54.0
- Supported · #59/183
- Basis
- BenchAlign lane · 6 vs 4 public rows
- Reading
- GLM-5.2 leads · intervals overlap
Coding
Directional only- GLM-5.2
- 61.0
- Supported · #19/152
- MiMo-V2.5-Pro
- 55.4
- Estimated · #40/152
- Basis
- BenchAlign lane · 8 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5.2
- 89.8
- #22/123
- MiMo-V2.5-Pro
- 93.7
- #6/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- MiMo-V2.5-Pro
- 75.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- MiMo-V2.5-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | MiMo-V2.5-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.5Supported · #29/153 | 39.8Supported · #119/153 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | GLM-5.2 leads |
| Knowledge | 60.7Supported · #35/183 | 54.0Supported · #59/183 | Like-for-likeBenchAlign lane · 6 vs 4 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 61.0Supported · #19/152 | 55.4Estimated · #40/152 | Directional onlyBenchAlign lane · 8 vs 4 public rows | Directional only |
| Instruction following | 89.8#22/123 | 93.7#6/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 74.8Unranked · 2 rankable rows | 75.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 80.7Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 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) 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
LiveCodeBench (Vals)
Coding
HLE
Knowledge
HLE w/o tools
Knowledge
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-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro 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-Pro
- API rate not published
- Fits in one request
MiMo-V2.5-Pro 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-Pro
- 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-Pro 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-Pro
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
MiMo-V2.5-Pro
1M
API model ID
GLM-5.2
Not sourced
MiMo-V2.5-Pro
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-Pro
No comparable hosted API rate
Documented inputs
GLM-5.2
Not sourced
MiMo-V2.5-Pro
Not sourced
Documented outputs
GLM-5.2
Not sourced
MiMo-V2.5-Pro
Not sourced
Provider availability
GLM-5.2
Not sourced
MiMo-V2.5-Pro
Not sourced
Reasoning profile
GLM-5.2
Reasoning
MiMo-V2.5-Pro
Reasoning
Weight access
GLM-5.2
Open Weight
MiMo-V2.5-Pro
Proprietary
License
GLM-5.2
Open Weight
MiMo-V2.5-Pro
Proprietary
Release date
GLM-5.2
2026-06-16
MiMo-V2.5-Pro
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, 68.19 versus 62.92, 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 evidence28 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourceMiMo-V2.5-Pro68.4%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceMiMo-V2.5-Pro—Not directly comparable
Toolathlon
GLM-5.248.2%SourceMiMo-V2.5-Pro—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceMiMo-V2.5-Pro57.3%SourceGLM-5.2 leads this result
Claw-Eval
GLM-5.2—MiMo-V2.5-Pro63.8%SourceNot directly comparable
τ³-bench results
GLM-5.2—MiMo-V2.5-Pro72.9%SourceNot directly comparable
Gert Labs
GLM-5.2—MiMo-V2.5-Pro62.70%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceMiMo-V2.5-Pro57.2%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceMiMo-V2.5-Pro—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceMiMo-V2.5-Pro68.4%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceMiMo-V2.5-Pro—Not directly comparable
cursorBench32
GLM-5.255.0%SourceMiMo-V2.5-Pro—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceMiMo-V2.5-Pro—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourceMiMo-V2.5-Pro81.4%SourceMiMo-V2.5-Pro leads this result
SWE-bench (Vals)
GLM-5.282.8%SourceMiMo-V2.5-Pro74.0%SourceGLM-5.2 leads this result
Reasoning
CritPt
GLM-5.220.9%SourceMiMo-V2.5-Pro—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourceMiMo-V2.5-Pro—Not directly comparable
GPQA-D
GLM-5.291.2%SourceMiMo-V2.5-Pro—Not directly comparable
HLE
GLM-5.254.7%SourceMiMo-V2.5-Pro48%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceMiMo-V2.5-Pro34%SourceGLM-5.2 leads this result
GPQA Diamond (Vals)
GLM-5.285.6%SourceMiMo-V2.5-Pro82.6%SourceGLM-5.2 leads this result
MMLU-Pro (Vals)
GLM-5.286.7%SourceMiMo-V2.5-Pro84.6%SourceGLM-5.2 leads this result
Math
Frequently asked questions
Which is better, GLM-5.2 or MiMo-V2.5-Pro?
GLM-5.2 has the higher public score estimate, 68.19 versus 62.92, 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-Pro?
GLM-5.2 scores higher for coding on the public lane, 61 to 55.4. MiMo-V2.5-Pro 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-5.2 or MiMo-V2.5-Pro?
GLM-5.2 leads the public agentic tasks lane, 58.5 to 39.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GLM-5.2 or MiMo-V2.5-Pro?
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-Pro?
Both models list the same context window, 1M.
Related comparisons
Last updated September 14, 2026
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