Agentic
Not comparable- GLM-5.1
- 54.3
- Estimated · #37/152
- LLaDA2.2-mini
- Not ranked
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- Not comparable
GLM-5.1 vs LLaDA2.2-mini
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 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.
Long documents
Prompts that approach the documented context limit
GLM-5.1
GLM-5.1 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
LLaDA2.2-mini is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
LLaDA2.2-mini is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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
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. LLaDA2.2-mini 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. LLaDA2.2-mini has no comparable published API token rate.
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.
23 GLM-5.1 only
3 Shared
6 LLaDA2.2-mini only
- Shared results
- 3
- GLM-5.1 only
- 23
- LLaDA2.2-mini only
- 6
- Like-for-like categories
- 0 / 8
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.
Coding
Not comparable- GLM-5.1
- 56.4
- Supported · #38/151
- LLaDA2.2-mini
- Not ranked
- Basis
- BenchAlign lane · 7 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- LLaDA2.2-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.1
- 54.8
- Supported · #54/183
- LLaDA2.2-mini
- Not ranked
- Basis
- BenchAlign lane · 4 vs 1 public rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 64.1
- #3/7
- LLaDA2.2-mini
- Not ranked
- Basis
- Provisional lane · 4 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not ranked
- LLaDA2.2-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not ranked
- LLaDA2.2-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- 93.7
- #5/123
- LLaDA2.2-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GLM-5.1 | LLaDA2.2-mini | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.3Estimated · #37/152 | Not ranked | Not comparableBenchAlign lane · 9 vs 4 public rows | Not comparable |
| Coding | 56.4Supported · #38/151 | Not ranked | Not comparableBenchAlign lane · 7 vs 1 public rows | Not comparable |
| Reasoning | 71.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 54.8Supported · #54/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 1 public rows | Not comparable |
| Math | 64.1#3/7 | Not ranked | Not comparableProvisional lane · 4 vs 1 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 |
| Instruction following | 93.7#5/123 | Not ranked | Not comparableProvisional lane · 0 vs 1 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.
AIME26
Math
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-5.1
- $0.0036
- Fits in one request
- LLaDA2.2-mini
- Self-hosted; infrastructure cost varies
- Fits in one request
LLaDA2.2-mini has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.1
- $0.0832
- Fits in one request
- LLaDA2.2-mini
- Self-hosted; infrastructure cost varies
- Fits in one request
LLaDA2.2-mini has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
- LLaDA2.2-mini
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
GLM-5.1 does not fit this workload in one request. LLaDA2.2-mini 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. LLaDA2.2-mini 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-5.1LLaDA2.2-mini
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
LLaDA2.2-mini
API model ID
GLM-5.1
Not sourced
LLaDA2.2-mini
inclusionAI/LLaDA2.2-mini
InclusionAI LLaDA2.2-mini model cardCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
LLaDA2.2-mini
No comparable hosted API rate
InclusionAI LLaDA2.2-mini model cardDocumented inputs
GLM-5.1
Not sourced
LLaDA2.2-mini
Not sourced
Documented outputs
GLM-5.1
Not sourced
LLaDA2.2-mini
Not sourced
Provider availability
GLM-5.1
Not sourced
LLaDA2.2-mini
Not sourced
Reasoning profile
GLM-5.1
Reasoning
LLaDA2.2-mini
Reasoning
Weight access
GLM-5.1
Open Weight
LLaDA2.2-mini
Open Weight
License
GLM-5.1
Open Weight
LLaDA2.2-mini
Open Weight
Release date
GLM-5.1
2026-04-07
LLaDA2.2-mini
2026-07-16
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GLM-5.1 has the larger documented window (203K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
LLaDA2.2-mini
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence32 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceLLaDA2.2-mini—Not directly comparable
BrowseComp
GLM-5.168%SourceLLaDA2.2-mini—Not directly comparable
τ³-bench results
GLM-5.170.6%SourceLLaDA2.2-mini—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceLLaDA2.2-mini—Not directly comparable
CyberGym
GLM-5.168.7%SourceLLaDA2.2-mini—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceLLaDA2.2-mini57.2%SourceGLM-5.1 leads this result
Gert Labs
GLM-5.160.11%SourceLLaDA2.2-mini—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceLLaDA2.2-mini—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%SourceLLaDA2.2-mini—Not directly comparable
BFCL v4
GLM-5.1—LLaDA2.2-mini47.7%SourceNot directly comparable
τ²-bench results
GLM-5.1—LLaDA2.2-mini57.5%SourceNot directly comparable
PinchBench
GLM-5.1—LLaDA2.2-mini62.3%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceLLaDA2.2-mini—Not directly comparable
NL2Repo
GLM-5.142.7%SourceLLaDA2.2-mini—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceLLaDA2.2-mini—Not directly comparable
Vibe Code Bench
GLM-5.131.46%SourceLLaDA2.2-mini—Not directly comparable
OpenHarmony Bench
GLM-5.152.3%SourceLLaDA2.2-mini—Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%SourceLLaDA2.2-mini—Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%SourceLLaDA2.2-mini—Not directly comparable
LiveCodeBench v6
GLM-5.1—LLaDA2.2-mini28.1%SourceNot directly comparable
Reasoning
LongBench v2
GLM-5.1—LLaDA2.2-mini35.0%SourceNot directly comparable
Knowledge
Math
AIME26
GLM-5.195.3%SourceLLaDA2.2-mini35.0%SourceGLM-5.1 leads this result
HMMT Nov 2025
GLM-5.194.0%SourceLLaDA2.2-mini—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceLLaDA2.2-mini—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceLLaDA2.2-mini—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceLLaDA2.2-mini—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceLLaDA2.2-mini—Not directly comparable
Instruction following
IFBench
GLM-5.1—LLaDA2.2-mini24.9%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.1 or LLaDA2.2-mini?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.1 or LLaDA2.2-mini?
LLaDA2.2-mini is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5.1 or LLaDA2.2-mini?
LLaDA2.2-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5.1 or LLaDA2.2-mini?
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-5.1 or LLaDA2.2-mini?
GLM-5.1 has the larger documented context window: 203K, compared with 128K.
Related comparisons
Last updated September 10, 2026
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