Updated October 14, 2026. Rank says GLM-5.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
GLM-5.1 has the higher public score estimate, 56.31 versus 55, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 12 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
GLM-5.1
GLM-5.1 has the higher public coding point estimate, 47.3 to 36.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
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.
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.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GLM-5.1 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
BrowseCompAgentic
Normalized gap 15.5MMLU-Pro (Vals)Knowledge
Normalized gap 2.7LiveCodeBench (Vals)Coding
Normalized gap 0.8SWE-bench ProCoding
Normalized gap 0.6Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | GLM-5.1 | MiniMax M3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.3Supported · #48/149 | 36.5Supported · #73/149 | Like-for-likeBenchAlign v5.8 lane · 8 vs 10 public rows | GLM-5.1 leads · intervals overlap |
| Knowledge | 52.6Supported · #69/181 | 49.6Supported · #80/181 | Like-for-likeBenchAlign v5.8 lane · 4 vs 2 public rows | GLM-5.1 leads · intervals overlap |
| Agentic | 43.8Estimated · #60/125 | 39.6Supported · #66/125 | Directional onlyBenchAlign v5.8 lane · 10 vs 9 public rows | Directional only |
| Instruction following | 93.8#6/144 | 93.8#5/144 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 76.5Unranked · 2 rankable rows | 82.9#5/29 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 61.5#43/54 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 66.7#7/12 | Not ranked | Not comparableProvisional lane · 3 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
1K fresh input + 500 output tokens
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
MiniMax M3
1M
GLM-5.1
Not sourced
MiniMax M3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
MiniMax M3
$0.06 per 1M cached input tokens
GLM-5.1
Not sourced
MiniMax M3
Not sourced
GLM-5.1
Not sourced
MiniMax M3
Not sourced
GLM-5.1
Not sourced
MiniMax M3
Not sourced
GLM-5.1
Reasoning
MiniMax M3
Non-Reasoning
GLM-5.1
Open Weight
MiniMax M3
Open Weight
GLM-5.1
Open Weight
MiniMax M3
Open Weight
GLM-5.1
2026-04-07
MiniMax M3
2026-06-01
GLM-5.1 has the higher public score estimate, 56.31 versus 55, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GLM-5.1 has the higher public coding point estimate, 47.3 to 36.5, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.1 scores higher for agentic tasks on the public lane, 43.8 to 39.6. GLM-5.1 is 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.
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.0009 on MiniMax M3; repository review costs $0.0832 and $0.0186; the cache-heavy agent loop costs $0.352 and $0.03. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
MiniMax M3 has the larger documented context window: 1M, compared with 203K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
MiniMax M3 leads this result
τ³-bench results
Not directly comparable
MCP Atlas
MiniMax M3 leads this result
CyberGym
Not directly comparable
Claw-Eval
MiniMax M3 leads this result
Gert Labs
Not directly comparable
ResearchClawBench
Shared sourceMiniMax M3 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.1 leads this result
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
OSWorld-Verified
Not directly comparable
BankerToolBench
Not directly comparable
OSWorld 2.0
Not directly comparable
SWE-bench Pro
MiniMax M3 leads this result
NL2Repo
GLM-5.1 leads this result
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
OpenHarmony Bench
Shared sourceGLM-5.1 leads this result
LiveCodeBench (Vals)
MiniMax M3 leads this result
SWE-bench (Vals)
GLM-5.1 leads this result
SWE-bench Verified
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
VIBE V2
Not directly comparable
SVG-Bench
Not directly comparable
KernelBench Hard
Not directly comparable
OfficeQA Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
MMMU-Pro
Not directly comparable
VideoMMMU
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
USAMO 2026
Not directly comparable
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Last updated October 14, 2026
