Agentic
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Model comparison
GLM-5-Turbo vs Kimi K2
Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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-Turbo
GLM-5-Turbo 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-Turbo does not fit this workload in one request. Kimi K2 does not fit this workload in one request. GLM-5-Turbo 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.
1 GLM-5-Turbo only
2 Kimi K2 only
- Shared results
- 0
- GLM-5-Turbo only
- 1
- Kimi K2 only
- 2
- Like-for-like categories
- 0 / 8
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
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- 16.1
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5-Turbo
- Not measured
- Kimi K2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5-Turbo | Kimi K2 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | 16.1 | Not comparable0 vs 2 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.
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-Turbo
- $0.0032
- 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-Turbo
- $0.072
- 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-Turbo
- $0.304
- 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-Turbo does not fit this workload in one request. Kimi K2 does not fit this workload in one request. GLM-5-Turbo 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-TurboKimi K2
Context window
Maximum documented context; output-token limits may be lower.
GLM-5-Turbo
200K
Kimi K2
128K
API model ID
GLM-5-Turbo
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-Turbo
Not published
Kimi K2
Not published
Documented inputs
GLM-5-Turbo
Not sourced
Kimi K2
Not sourced
Documented outputs
GLM-5-Turbo
Not sourced
Kimi K2
Not sourced
Provider availability
GLM-5-Turbo
Not sourced
Kimi K2
Not sourced
Reasoning profile
GLM-5-Turbo
Reasoning
Kimi K2
Non-Reasoning
Weight access
GLM-5-Turbo
Proprietary
Kimi K2
Proprietary
License
GLM-5-Turbo
Proprietary
Kimi K2
Proprietary
Release date
GLM-5-Turbo
2026-03-01
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.072 vs $0.0375. Cache-heavy agent loop: $0.304 vs $0.157.
- Context tradeoff
- GLM-5-Turbo has the larger documented window (200K).
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.
Frequently asked questions
Which is better, GLM-5-Turbo or Kimi K2?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5-Turbo 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-Turbo 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-Turbo or Kimi K2?
For the stated presets, chat costs $0.0032 on GLM-5-Turbo and $0.00185 on Kimi K2; repository review costs $0.072 and $0.0375; the cache-heavy agent loop costs $0.304 and $0.157. GLM-5-Turbo does not fit this workload in one request. Kimi K2 does not fit this workload in one request. GLM-5-Turbo 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-Turbo or Kimi K2?
GLM-5-Turbo has the larger documented context window: 200K, compared with 128K.
Related comparisons
Last updated July 29, 2026
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