Agentic
Directional only- GLM-5
- 51.0
- Estimated · #45/152
- Kimi K2.7 Code
- 46.8
- Estimated · #72/152
- Basis
- BenchAlign lane · 11 vs 3 public rows
- Reading
- Directional only
GLM-5 vs Kimi K2.7 Code
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K2.7 Code has the higher public score estimate, 65.49 versus 61.45, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
Kimi K2.7 Code
Kimi K2.7 Code has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GLM-5
GLM-5 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 K2.7 Code
Kimi K2.7 Code 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
GLM-5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5 and Kimi K2.7 Code are scored on Estimated evidence for agentic, so the reading is 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. Kimi K2.7 Code 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.
35 GLM-5 only
1 Shared
7 Kimi K2.7 Code only
- Shared results
- 1
- GLM-5 only
- 35
- Kimi K2.7 Code only
- 7
- Like-for-like categories
- 0 / 8
4 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.
Coding
Directional only- GLM-5
- 56.1
- Estimated · #39/151
- Kimi K2.7 Code
- 50.9
- Supported · #51/151
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5
- 54.1
- Estimated · #57/183
- Kimi K2.7 Code
- 61.6
- Estimated · #31/183
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GLM-5
- 88.5
- #31/123
- Kimi K2.7 Code
- 76.6
- #58/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5
- 52.1
- Unranked · 4 rankable rows
- Kimi K2.7 Code
- 75.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5
- 56.9
- #7/7
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5
- 48.7
- #6/12
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5
- Not ranked
- Kimi K2.7 Code
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5 | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 51.0Estimated · #45/152 | 46.8Estimated · #72/152 | Directional onlyBenchAlign lane · 11 vs 3 public rows | Directional only |
| Coding | 56.1Estimated · #39/151 | 50.9Supported · #51/151 | Directional onlyBenchAlign lane · 6 vs 5 public rows | Directional only |
| Knowledge | 54.1Estimated · #57/183 | 61.6Estimated · #31/183 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Instruction following | 88.5#31/123 | 76.6#58/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 52.1Unranked · 4 rankable rows | 75.5Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 56.9#7/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | 48.7#6/12 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- Kimi K2.7 Code
- $0.00295
- Fits in one request
GLM-5 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
- Kimi K2.7 Code
- $0.0595
- Fits in one request
Kimi K2.7 Code 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
- Kimi K2.7 Code
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
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. Kimi K2.7 Code 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-5Kimi K2.7 Code
Context window
Maximum documented context; output-token limits may be lower.
GLM-5
200K
Kimi K2.7 Code
256K
API model ID
GLM-5
Not sourced
Kimi K2.7 Code
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
Kimi K2.7 Code
Not published
Documented inputs
GLM-5
Not sourced
Kimi K2.7 Code
Not sourced
Documented outputs
GLM-5
Not sourced
Kimi K2.7 Code
Not sourced
Provider availability
GLM-5
Not sourced
Kimi K2.7 Code
Not sourced
Reasoning profile
GLM-5
Non-Reasoning
Kimi K2.7 Code
Reasoning
Weight access
GLM-5
Open Weight
Kimi K2.7 Code
Open Weight
License
GLM-5
Open Weight
Kimi K2.7 Code
Open Weight
Release date
GLM-5
2026-03-01
Kimi K2.7 Code
2026-06-12
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
- Kimi K2.7 Code has the higher public score estimate, 65.49 versus 61.45, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0596 vs $0.0595. Cache-heavy agent loop: $0.252 vs $0.249.
- Context tradeoff
- Kimi K2.7 Code has the larger documented window (256K).
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
API / mo$3,150
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence43 rows
Agentic
Terminal-Bench 2.0
GLM-556.2%SourceKimi K2.7 Code—Not directly comparable
Claw-Eval
GLM-557.7%SourceKimi K2.7 Code—Not directly comparable
QwenClawBench
GLM-554.1%SourceKimi K2.7 Code—Not directly comparable
τ³-bench results
GLM-565.6%SourceKimi K2.7 Code—Not directly comparable
DeepPlanning
GLM-514.6%SourceKimi K2.7 Code—Not directly comparable
Toolathlon
GLM-538%SourceKimi K2.7 Code—Not directly comparable
MCP Atlas
GLM-531.1%SourceKimi K2.7 Code76%SourceKimi K2.7 Code leads this result
MCP-Tasks
GLM-560.8%SourceKimi K2.7 Code—Not directly comparable
WideResearch
GLM-569.8%SourceKimi K2.7 Code—Not directly comparable
CyberGym
GLM-543.2%SourceKimi K2.7 Code—Not directly comparable
Gert Labs
GLM-550.99%SourceKimi K2.7 Code—Not directly comparable
Kimi Claw 24/7
GLM-5—Kimi K2.7 Code46.9%SourceNot directly comparable
MCP Mark Verified
GLM-5—Kimi K2.7 Code81.1%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-577.8%SourceKimi K2.7 Code—Not directly comparable
SWE-bench Verified*
GLM-572.8%SourceKimi K2.7 Code—Not directly comparable
SWE-bench Pro
GLM-555.1%SourceKimi K2.7 Code—Not directly comparable
SWE Multilingual
GLM-573.3%SourceKimi K2.7 Code—Not directly comparable
SWE-Rebench
GLM-562.8%SourceKimi K2.7 Code—Not directly comparable
React Native Evals
GLM-574.8%SourceKimi K2.7 Code—Not directly comparable
Kimi Code Bench v2
GLM-5—Kimi K2.7 Code62.0%SourceNot directly comparable
ProgramBench
GLM-5—Kimi K2.7 Code53.6%SourceNot directly comparable
MLS-Bench Lite
GLM-5—Kimi K2.7 Code35.1%SourceNot directly comparable
cursorBench32
GLM-5—Kimi K2.7 Code49.7%SourceNot directly comparable
OpenHarmony Bench
GLM-5—Kimi K2.7 Code52.1%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-586%SourceKimi K2.7 Code—Not directly comparable
GPQA-D
GLM-586.0%SourceKimi K2.7 Code—Not directly comparable
SuperGPQA
GLM-566.8%SourceKimi K2.7 Code—Not directly comparable
MMLU-Pro
GLM-585.7%SourceKimi K2.7 Code—Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%SourceKimi K2.7 Code—Not directly comparable
HLE
GLM-550.4%SourceKimi K2.7 Code—Not directly comparable
Math
AIME26
GLM-595.8%SourceKimi K2.7 Code—Not directly comparable
AIME25 (Arcee)
GLM-593.3%SourceKimi K2.7 Code—Not directly comparable
HMMT Feb 2025
GLM-597.5%SourceKimi K2.7 Code—Not directly comparable
HMMT Nov 2025
GLM-596.9%SourceKimi K2.7 Code—Not directly comparable
HMMT Feb 2026
GLM-586.4%SourceKimi K2.7 Code—Not directly comparable
MMAnswerBench
GLM-582.5%SourceKimi K2.7 Code—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-516.434%SourceKimi K2.7 Code—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-52.100%SourceKimi K2.7 Code—Not directly comparable
Multilingual
Instruction following
IFEval
GLM-592.6%SourceKimi K2.7 Code—Not directly comparable
Frequently asked questions
Which is better, GLM-5 or Kimi K2.7 Code?
Kimi K2.7 Code has the higher public score estimate, 65.49 versus 61.45, 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 Kimi K2.7 Code?
GLM-5 scores higher for coding on the public lane, 56.1 to 50.9. GLM-5 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 or Kimi K2.7 Code?
GLM-5 scores higher for agentic tasks on the public lane, 51 to 46.8. GLM-5 and Kimi K2.7 Code are 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.
Which costs less, GLM-5 or Kimi K2.7 Code?
For the stated presets, chat costs $0.0026 on GLM-5 and $0.00295 on Kimi K2.7 Code; repository review costs $0.0596 and $0.0595; the cache-heavy agent loop costs $0.252 and $0.249. 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. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5 or Kimi K2.7 Code?
Kimi K2.7 Code has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated September 10, 2026
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