Agentic
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- 73.5
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
GLM-5 (Reasoning) vs Kimi K2.6
Updated August 27, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5 (Reasoning) has the higher public score estimate, 60.47 versus 60.14, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
Kimi K2.6
Kimi K2.6 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GLM-5 (Reasoning)
GLM-5 (Reasoning) 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 K2.6
Kimi K2.6 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 (Reasoning) does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 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 Shared
31 Kimi K2.6 only
- Shared results
- 1
- GLM-5 (Reasoning) only
- 0
- Kimi K2.6 only
- 31
- 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 (Reasoning)
- Not measured
- Kimi K2.6
- 64.4
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- 42.2
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- 67.1
- Weighted basis
- 0 vs 4 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- 79.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5 (Reasoning)
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5 (Reasoning) | Kimi K2.6 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 73.5 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 64.4 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 42.2 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 67.1 | Not comparable0 vs 4 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 79.8 | Not comparable0 vs 2 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 (Reasoning)
- $0.0026
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
GLM-5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5 (Reasoning)
- $0.0596
- Fits in one request
- Kimi K2.6
- $0.0595
- Fits in one request
Kimi K2.6 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 (Reasoning)
- $0.252
- Does not fit in one request
- Cached input priced at the published list-input rate
- Kimi K2.6
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5 (Reasoning) does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 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 (Reasoning)Kimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
GLM-5 (Reasoning)
200K
Kimi K2.6
256K
API model ID
GLM-5 (Reasoning)
Not sourced
Kimi K2.6
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 (Reasoning)
Not published
Kimi K2.6
Not published
Documented inputs
GLM-5 (Reasoning)
Not sourced
Kimi K2.6
Not sourced
Documented outputs
GLM-5 (Reasoning)
Not sourced
Kimi K2.6
Not sourced
Provider availability
GLM-5 (Reasoning)
Not sourced
Kimi K2.6
Not sourced
Reasoning profile
GLM-5 (Reasoning)
Reasoning
Kimi K2.6
Reasoning
Weight access
GLM-5 (Reasoning)
Open Weight
Kimi K2.6
Open Weight
License
GLM-5 (Reasoning)
Open Weight
Kimi K2.6
Open Weight
Release date
GLM-5 (Reasoning)
2026-03-01
Kimi K2.6
2026-04-20
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 (Reasoning) has the higher public score estimate, 60.47 versus 60.14, 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 K2.6 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 (Reasoning)
API / mo$3,150
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
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 evidence32 rows
Agentic
Terminal-Bench 2.0
GLM-5 (Reasoning)—Kimi K2.666.7%SourceNot directly comparable
BrowseComp
GLM-5 (Reasoning)—Kimi K2.683.2%SourceNot directly comparable
OSWorld-Verified
GLM-5 (Reasoning)—Kimi K2.673.1%SourceNot directly comparable
Toolathlon
GLM-5 (Reasoning)—Kimi K2.650%SourceNot directly comparable
MCP Atlas
GLM-5 (Reasoning)—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
GLM-5 (Reasoning)—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
GLM-5 (Reasoning)—Kimi K2.692.5%SourceNot directly comparable
WideResearch
GLM-5 (Reasoning)—Kimi K2.680.8%SourceNot directly comparable
Gert Labs
GLM-5 (Reasoning)—Kimi K2.656.82%SourceNot directly comparable
ResearchClawBench
GLM-5 (Reasoning)—Kimi K2.618.0%SourceNot directly comparable
OSWorld 2.0
GLM-5 (Reasoning)—Kimi K2.64.6%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGLM-5 (Reasoning)23.36%Kimi K2.637.89%Kimi K2.6 leads this result
SWE-bench Verified
GLM-5 (Reasoning)—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
GLM-5 (Reasoning)—Kimi K2.689.6%SourceNot directly comparable
SWE-bench Pro
GLM-5 (Reasoning)—Kimi K2.658.6%SourceNot directly comparable
SWE Multilingual
GLM-5 (Reasoning)—Kimi K2.676.7%SourceNot directly comparable
SciCode
GLM-5 (Reasoning)—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
GLM-5 (Reasoning)—Kimi K2.666.7%SourceNot directly comparable
cursorBench31
GLM-5 (Reasoning)—Kimi K2.647.6%SourceNot directly comparable
Knowledge
Math
AIME26
GLM-5 (Reasoning)—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
GLM-5 (Reasoning)—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
GLM-5 (Reasoning)—Kimi K2.686.0%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5 (Reasoning)—Kimi K2.638.966%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GLM-5 (Reasoning)—Kimi K2.614.580%SourceNot directly comparable
Multimodal
MMMU-Pro
GLM-5 (Reasoning)—Kimi K2.679.4%SourceNot directly comparable
MMMU-Pro w/ Python
GLM-5 (Reasoning)—Kimi K2.680.1%SourceNot directly comparable
CharXiv
GLM-5 (Reasoning)—Kimi K2.680.4%SourceNot directly comparable
MathVision
GLM-5 (Reasoning)—Kimi K2.687.4%SourceNot directly comparable
V*
GLM-5 (Reasoning)—Kimi K2.696.9%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5 (Reasoning) or Kimi K2.6?
GLM-5 (Reasoning) has the higher public score estimate, 60.47 versus 60.14, 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 (Reasoning) or Kimi K2.6?
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 (Reasoning) or Kimi K2.6?
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 (Reasoning) or Kimi K2.6?
For the stated presets, chat costs $0.0026 on GLM-5 (Reasoning) and $0.00295 on Kimi K2.6; repository review costs $0.0596 and $0.0595; the cache-heavy agent loop costs $0.252 and $0.249. GLM-5 (Reasoning) does not fit this workload in one request. GLM-5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5 (Reasoning) or Kimi K2.6?
Kimi K2.6 has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated August 27, 2026
Watch GLM-5 (Reasoning) vs Kimi K2.6
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.