Math
Directional only- GLM-5.1
- 62.0
- Kimi K2
- 16.1
- Weighted basis
- 4 vs 2 rows
- Reading
- Directional only
Model comparison
GLM-5.1 vs Kimi K2
Updated August 7, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GLM-5.1 has the higher public score, 66.9 versus 26.31, and the 90% score intervals do not overlap.
2 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
GLM-5.1
GLM-5.1 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2
Kimi K2 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
Kimi K2
Kimi K2 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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-5.1 does not fit this workload in one request. Kimi K2 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. Kimi K2 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.
18 GLM-5.1 only
2 Shared
- Shared results
- 2
- GLM-5.1 only
- 18
- Kimi K2 only
- 0
- Like-for-like categories
- 0 / 8
1 category uses 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.
Agentic
Not comparable- GLM-5.1
- 65.4
- Kimi K2
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5.1
- 61.3
- Kimi K2
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.1
- 52.3
- Kimi K2
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.1 | Kimi K2 | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 62.0 | 16.1 | Directional only4 vs 2 rows | Directional only |
| Agentic | 65.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 61.3 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 52.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 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.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
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
- Kimi K2
- $0.00185
- Fits in one request
Kimi K2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.1
- $0.0832
- Fits in one request
- Kimi K2
- $0.0375
- Fits in one request
Kimi K2 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-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
- Kimi K2
- $0.157
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-5.1 does not fit this workload in one request. Kimi K2 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. Kimi K2 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-5.1Kimi K2
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Kimi K2
128K
API model ID
GLM-5.1
Not sourced
Kimi K2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
Kimi K2
Not published
Documented inputs
GLM-5.1
Not sourced
Kimi K2
Not sourced
Documented outputs
GLM-5.1
Not sourced
Kimi K2
Not sourced
Provider availability
GLM-5.1
Not sourced
Kimi K2
Not sourced
Reasoning profile
GLM-5.1
Reasoning
Kimi K2
Non-Reasoning
Weight access
GLM-5.1
Open Weight
Kimi K2
Proprietary
License
GLM-5.1
Open Weight
Kimi K2
Proprietary
Release date
GLM-5.1
2026-04-07
Kimi K2
2025-07-01
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
- GLM-5.1 has the higher public score, 66.9 versus 26.31, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0832 vs $0.0375. Cache-heavy agent loop: $0.352 vs $0.157.
- 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
Kimi K2
API / mo$2,325
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 evidence20 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceKimi K2—Not directly comparable
BrowseComp
GLM-5.168%SourceKimi K2—Not directly comparable
τ³-bench results
GLM-5.170.6%SourceKimi K2—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceKimi K2—Not directly comparable
CyberGym
GLM-5.168.7%SourceKimi K2—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceKimi K2—Not directly comparable
Gert Labs
GLM-5.160.11%SourceKimi K2—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceKimi K2—Not directly comparable
Coding
Knowledge
Math
AIME26
GLM-5.195.3%SourceKimi K2—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceKimi K2—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceKimi K2—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceKimi K2—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGLM-5.133.448%Kimi K221.404%GLM-5.1 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGLM-5.112.500%Kimi K20.000%GLM-5.1 leads this result
Frequently asked questions
Which is better, GLM-5.1 or Kimi K2?
GLM-5.1 has the higher public score, 66.9 versus 26.31, 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-5.1 or Kimi K2?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GLM-5.1 or Kimi K2?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GLM-5.1 or Kimi K2?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.00185 on Kimi K2; repository review costs $0.0832 and $0.0375; the cache-heavy agent loop costs $0.352 and $0.157. GLM-5.1 does not fit this workload in one request. Kimi K2 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. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.1 or Kimi K2?
GLM-5.1 has the larger documented context window: 203K, compared with 128K.
Related comparisons
Last updated August 7, 2026
Watch GLM-5.1 vs Kimi K2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.