Agentic
Directional only- GLM-5
- 56.2
- MiniMax M3
- 72.3
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
GLM-5 vs MiniMax M3
Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
MiniMax M3 has the higher public score estimate, 68.73 versus 65.89, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
5 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.
Long documents
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
MiniMax M3
MiniMax M3 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
Repository review cost
50K fresh input + 3K output tokens
MiniMax M3
MiniMax M3 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
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.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
31 GLM-5 only
5 Shared
17 MiniMax M3 only
- Shared results
- 5
- GLM-5 only
- 31
- MiniMax M3 only
- 17
- Like-for-like categories
- 0 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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
Directional only- GLM-5
- 66.3
- MiniMax M3
- 72.2
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5
- 60.8
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5
- 66.4
- MiniMax M3
- Not measured
- Weighted basis
- 4 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5
- 56.3
- MiniMax M3
- 85.7
- Weighted basis
- 4 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5
- 83.1
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not measured
- MiniMax M3
- 64.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5
- 92.6
- MiniMax M3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5 | MiniMax M3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 56.2 | 72.3 | Directional only1 vs 3 rows | Directional only |
| Coding | 66.3 | 72.2 | Directional only3 vs 2 rows | Directional only |
| Reasoning | 60.8 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 66.4 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Math | 56.3 | 85.7 | Not comparable4 vs 1 rows | Not comparable |
| Multilingual | 83.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multimodal | Not measured | 64.9 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | 92.6 | Not measured | Not comparable1 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
SWE-bench Verified
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
- MiniMax M3
- $0.0009
- Fits in one request
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5
- $0.0596
- Fits in one request
- MiniMax M3
- $0.0186
- Fits in one request
MiniMax M3 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
- $0.252
- Does not fit in one request
- Cached input priced at the published list-input rate
- MiniMax M3
- $0.03
- Fits 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5MiniMax M3
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
MiniMax M3
1M
API model ID
GLM-5
Not sourced
MiniMax M3
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
MiniMax M3
$0.06 per 1M cached input tokens
Documented inputs
GLM-5
Not sourced
MiniMax M3
Not sourced
Documented outputs
GLM-5
Not sourced
MiniMax M3
Not sourced
Provider availability
GLM-5
Not sourced
MiniMax M3
Not sourced
Reasoning profile
GLM-5
Non-Reasoning
MiniMax M3
Non-Reasoning
Weight access
GLM-5
Open Weight
MiniMax M3
Open Weight
License
GLM-5
Open Weight
MiniMax M3
Open Weight
Release date
GLM-5
2026-03-01
MiniMax M3
2026-06-01
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
- MiniMax M3 has the higher public score estimate, 68.73 versus 65.89, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0596 vs $0.0186. Cache-heavy agent loop: $0.252 vs $0.03.
- Context tradeoff
- MiniMax M3 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 evidence53 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceMiniMax M366%SourceMiniMax M3 leads this result
Claw-Eval
GLM-557.7%SourceMiniMax M374.5%SourceMiniMax M3 leads this result
QwenClawBench
GLM-554.1%SourceMiniMax M3—Not directly comparable
τ³-bench results
GLM-565.6%SourceMiniMax M3—Not directly comparable
DeepPlanning
GLM-514.6%SourceMiniMax M3—Not directly comparable
Toolathlon
GLM-538%SourceMiniMax M3—Not directly comparable
MCP Atlas
GLM-531.1%SourceMiniMax M374.2%SourceMiniMax M3 leads this result
MCP-Tasks
GLM-560.8%SourceMiniMax M3—Not directly comparable
WideResearch
GLM-569.8%SourceMiniMax M3—Not directly comparable
CyberGym
GLM-543.2%SourceMiniMax M3—Not directly comparable
Gert Labs
GLM-550.99%SourceMiniMax M3—Not directly comparable
BrowseComp
GLM-5—MiniMax M383.5%SourceNot directly comparable
OSWorld-Verified
GLM-5—MiniMax M370.1%SourceNot directly comparable
BankerToolBench
GLM-5—MiniMax M376.1%SourceNot directly comparable
ResearchClawBench
GLM-5—MiniMax M319.8%SourceNot directly comparable
OSWorld 2.0
GLM-5—MiniMax M34.6%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceMiniMax M380.5%SourceMiniMax M3 leads this result
SWE-bench Verified*
GLM-572.8%SourceMiniMax M3—Not directly comparable
SWE-bench Pro
GLM-555.1%SourceMiniMax M359%SourceMiniMax M3 leads this result
SWE Multilingual
GLM-573.3%SourceMiniMax M3—Not directly comparable
SWE-Rebench
GLM-562.8%SourceMiniMax M3—Not directly comparable
React Native Evals
GLM-574.8%SourceMiniMax M3—Not directly comparable
Terminal-Bench 2.0
GLM-5—MiniMax M366.0%SourceNot directly comparable
NL2Repo
GLM-5—MiniMax M342.1%SourceNot directly comparable
VIBE V2
GLM-5—MiniMax M350.1%SourceNot directly comparable
SVG-Bench
GLM-5—MiniMax M363.7%SourceNot directly comparable
KernelBench Hard
GLM-5—MiniMax M328.8%SourceNot directly comparable
OpenHarmony Bench
GLM-5—MiniMax M348.4%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-586%SourceMiniMax M3—Not directly comparable
GPQA-D
GLM-586.0%SourceMiniMax M3—Not directly comparable
SuperGPQA
GLM-566.8%SourceMiniMax M3—Not directly comparable
MMLU-Pro
GLM-585.7%SourceMiniMax M3—Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%SourceMiniMax M3—Not directly comparable
HLE
GLM-550.4%SourceMiniMax M3—Not directly comparable
Math
AIME26
GLM-595.8%SourceMiniMax M3—Not directly comparable
AIME25 (Arcee)
GLM-593.3%SourceMiniMax M3—Not directly comparable
HMMT Feb 2025
GLM-597.5%SourceMiniMax M3—Not directly comparable
HMMT Nov 2025
GLM-596.9%SourceMiniMax M3—Not directly comparable
HMMT Feb 2026
GLM-586.4%SourceMiniMax M3—Not directly comparable
MMAnswerBench
GLM-582.5%SourceMiniMax M3—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-516.434%SourceMiniMax M3—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-52.100%SourceMiniMax M3—Not directly comparable
USAMO 2026
GLM-5—MiniMax M385.7%SourceNot directly comparable
Multilingual
Multimodal
OfficeQA Pro
GLM-5—MiniMax M345.1%SourceNot directly comparable
OmniDocBench 1.5
GLM-5—MiniMax M391.6%SourceNot directly comparable
MMMU-Pro
GLM-5—MiniMax M378.1%SourceNot directly comparable
VideoMMMU
GLM-5—MiniMax M384.6%SourceNot directly comparable
Video-MME (with subtitle)
GLM-5—MiniMax M385.4%SourceNot directly comparable
Instruction following
IFEval
GLM-592.6%SourceMiniMax M3—Not directly comparable
Frequently asked questions
Which is better, GLM-5 or MiniMax M3?
MiniMax M3 has the higher public score estimate, 68.73 versus 65.89, 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 MiniMax M3?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5 or MiniMax M3?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GLM-5 or MiniMax M3?
For the stated presets, chat costs $0.0026 on GLM-5 and $0.0009 on MiniMax M3; repository review costs $0.0596 and $0.0186; the cache-heavy agent loop costs $0.252 and $0.03. 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.
Which has the larger context window, GLM-5 or MiniMax M3?
MiniMax M3 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated August 30, 2026
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