Agentic
Like-for-like- GLM-5.2
- 58.5
- Supported · #29/153
- Mistral Medium 3.5 128B
- 22.2
- Supported · #151/153
- Basis
- BenchAlign lane · 6 vs 3 public rows
- Reading
- GLM-5.2 leads
GLM-5.2 vs Mistral Medium 3.5 128B
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, 68.19 versus 42.45, and the 90% score intervals do not overlap.
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.
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 22.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GLM-5.2
GLM-5.2 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
21 GLM-5.2 only
4 Shared
3 Mistral Medium 3.5 128B only
- Shared results
- 4
- GLM-5.2 only
- 21
- Mistral Medium 3.5 128B 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
- Mistral Medium 3.5 128B
- 40.5
- Supported · #132/183
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- GLM-5.2 leads · intervals overlap
Coding
Directional only- GLM-5.2
- 61.0
- Supported · #19/152
- Mistral Medium 3.5 128B
- 39.1
- Estimated · #121/152
- Basis
- BenchAlign lane · 8 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5.2
- 89.8
- #22/123
- Mistral Medium 3.5 128B
- 84.0
- #48/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- 68.6
- 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
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- Mistral Medium 3.5 128B
- 55.6
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.5Supported · #29/153 | 22.2Supported · #151/153 | Like-for-likeBenchAlign lane · 6 vs 3 public rows | GLM-5.2 leads |
| Knowledge | 60.7Supported · #35/183 | 40.5Supported · #132/183 | Like-for-likeBenchAlign lane · 6 vs 2 public rows | GLM-5.2 leads · intervals overlap |
| Coding | 61.0Supported · #19/152 | 39.1Estimated · #121/152 | Directional onlyBenchAlign lane · 8 vs 2 public rows | Directional only |
| Instruction following | 89.8#22/123 | 84.0#48/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 74.8Unranked · 2 rankable rows | 68.6Unranked · 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 | 55.6Unranked · 1 rankable row | 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.
MMLU-Pro (Vals)
Knowledge
GLM-5.2: 86.7%Mistral Medium 3.5 128B: 75.3%Normalized gap 11.4GLM-5.2 source Mistral Medium 3.5 128B source
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
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
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
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5.2 has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.2Mistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Mistral Medium 3.5 128B
256K
API model ID
GLM-5.2
Not sourced
Mistral Medium 3.5 128B
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
Mistral Medium 3.5 128B
Not published
Documented inputs
GLM-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
GLM-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
GLM-5.2
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
GLM-5.2
Open Weight
Mistral Medium 3.5 128B
Open Weight
License
GLM-5.2
Open Weight
Mistral Medium 3.5 128B
Open Weight
Release date
GLM-5.2
2026-06-16
Mistral Medium 3.5 128B
2026-04-29
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, 68.19 versus 42.45, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0832 vs $0.0975. Cache-heavy agent loop: $0.352 vs $0.405.
- Context tradeoff
- GLM-5.2 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 evidence28 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourceMistral Medium 3.5 128B—Not directly comparable
MCP Atlas
GLM-5.276.8%SourceMistral Medium 3.5 128B—Not directly comparable
Toolathlon
GLM-5.248.2%SourceMistral Medium 3.5 128B—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourceMistral Medium 3.5 128B39.0%SourceGLM-5.2 leads this result
τ³-bench results
GLM-5.2—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Gert Labs
GLM-5.2—Mistral Medium 3.5 128B39.10%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceMistral Medium 3.5 128B—Not directly comparable
NL2Repo
GLM-5.248.9%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceMistral Medium 3.5 128B—Not directly comparable
ProgramBench
GLM-5.263.7%SourceMistral Medium 3.5 128B—Not directly comparable
cursorBench32
GLM-5.255.0%SourceMistral Medium 3.5 128B—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceMistral Medium 3.5 128B—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourceMistral Medium 3.5 128B—Not directly comparable
SWE-bench (Vals)
GLM-5.282.8%SourceMistral Medium 3.5 128B66.4%SourceGLM-5.2 leads this result
SWE-bench Verified
GLM-5.2—Mistral Medium 3.5 128B77.6%SourceNot directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceMistral Medium 3.5 128B—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourceMistral Medium 3.5 128B—Not directly comparable
GPQA-D
GLM-5.291.2%SourceMistral Medium 3.5 128B—Not directly comparable
HLE
GLM-5.254.7%SourceMistral Medium 3.5 128B—Not directly comparable
HLE w/o tools
GLM-5.240.5%SourceMistral Medium 3.5 128B—Not directly comparable
GPQA Diamond (Vals)
GLM-5.285.6%SourceMistral Medium 3.5 128B34.8%SourceGLM-5.2 leads this result
MMLU-Pro (Vals)
GLM-5.286.7%SourceMistral Medium 3.5 128B75.3%SourceGLM-5.2 leads this result
Math
AIME26
GLM-5.299.2%SourceMistral Medium 3.5 128B—Not directly comparable
HMMT Nov 2025
GLM-5.294.4%SourceMistral Medium 3.5 128B—Not directly comparable
HMMT Feb 2026
GLM-5.292.5%SourceMistral Medium 3.5 128B—Not directly comparable
MMAnswerBench
GLM-5.291.0%SourceMistral Medium 3.5 128B—Not directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Mistral Medium 3.5 128B?
GLM-5.2 has the higher public score, 68.19 versus 42.45, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GLM-5.2 or Mistral Medium 3.5 128B?
GLM-5.2 scores higher for coding on the public lane, 61 to 39.1. Mistral Medium 3.5 128B 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 Mistral Medium 3.5 128B?
GLM-5.2 leads the public agentic tasks lane, 58.5 to 22.2, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GLM-5.2 or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0832 and $0.0975; the cache-heavy agent loop costs $0.352 and $0.405. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.2 or Mistral Medium 3.5 128B?
GLM-5.2 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 14, 2026
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