Knowledge
Directional only- GLM-5-Turbo
- 53.9
- Estimated · #62/183
- Kimi K2.5
- 52.0
- Estimated · #69/183
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Directional only
GLM-5-Turbo vs Kimi K2.5
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GLM-5-Turbo has the higher public score estimate, 61.64 versus 54.01, 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 K2.5
Kimi K2.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.5
Kimi K2.5 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 K2.5
Kimi K2.5 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-Turbo 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
GLM-5-Turbo is not ranked on the public lane for agentic, so no winner is named for agentic.
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. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 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
44 Kimi K2.5 only
- Shared results
- 1
- GLM-5-Turbo only
- 0
- Kimi K2.5 only
- 44
- Like-for-like categories
- 0 / 8
2 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.
Instruction following
Directional only- GLM-5-Turbo
- 89.7
- #24/123
- Kimi K2.5
- 85.8
- #41/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GLM-5-Turbo
- Not ranked
- Kimi K2.5
- 46.4
- Estimated · #77/152
- Basis
- BenchAlign lane · 1 vs 14 public rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5-Turbo
- Not ranked
- Kimi K2.5
- 50.6
- Estimated · #55/151
- Basis
- BenchAlign lane · 0 vs 8 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5-Turbo
- 70.3
- Unranked · 2 rankable rows
- Kimi K2.5
- 54.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5-Turbo
- Not ranked
- Kimi K2.5
- 62.4
- #5/7
- Basis
- Provisional lane · 0 vs 4 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5-Turbo
- Not ranked
- Kimi K2.5
- 38.2
- #8/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5-Turbo
- Not ranked
- Kimi K2.5
- 65.7
- #24/48
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | GLM-5-Turbo | Kimi K2.5 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 53.9Estimated · #62/183 | 52.0Estimated · #69/183 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Instruction following | 89.7#24/123 | 85.8#41/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 46.4Estimated · #77/152 | Not comparableBenchAlign lane · 1 vs 14 public rows | Not comparable |
| Coding | Not ranked | 50.6Estimated · #55/151 | Not comparableBenchAlign lane · 0 vs 8 public rows | Not comparable |
| Reasoning | 70.3Unranked · 2 rankable rows | 54.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 62.4#5/7 | Not comparableProvisional lane · 0 vs 4 weighted rows | Not comparable |
| Multilingual | Not ranked | 38.2#8/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 65.7#24/48 | 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.
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 K2.5
- $0.0021
- Fits in one request
Kimi K2.5 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 K2.5
- $0.039
- Fits in one request
Kimi K2.5 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 K2.5
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5-Turbo 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 K2.5 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 K2.5
Context window
Maximum documented context; output-token limits may be lower.
GLM-5-Turbo
200K
Kimi K2.5
256K
API model ID
GLM-5-Turbo
Not sourced
Kimi K2.5
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 K2.5
Not published
Documented inputs
GLM-5-Turbo
Not sourced
Kimi K2.5
Not sourced
Documented outputs
GLM-5-Turbo
Not sourced
Kimi K2.5
Not sourced
Provider availability
GLM-5-Turbo
Not sourced
Kimi K2.5
Not sourced
Reasoning profile
GLM-5-Turbo
Reasoning
Kimi K2.5
Non-Reasoning
Weight access
GLM-5-Turbo
Proprietary
Kimi K2.5
Open Weight
License
GLM-5-Turbo
Proprietary
Kimi K2.5
Open Weight
Release date
GLM-5-Turbo
2026-03-01
Kimi K2.5
2026-02-01
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-Turbo has the higher public score estimate, 61.64 versus 54.01, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.072 vs $0.039. Cache-heavy agent loop: $0.304 vs $0.162.
- Context tradeoff
- Kimi K2.5 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-Turbo
API / mo$3,900
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/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 evidence45 rows
Agentic
Claw-Eval
Shared sourceGLM-5-Turbo55.8%Kimi K2.552.3%GLM-5-Turbo leads this result
Terminal-Bench 2.0
GLM-5-Turbo—Kimi K2.550.8%SourceNot directly comparable
BrowseComp
GLM-5-Turbo—Kimi K2.560.6%SourceNot directly comparable
QwenClawBench
GLM-5-Turbo—Kimi K2.554.3%SourceNot directly comparable
τ³-bench results
GLM-5-Turbo—Kimi K2.565.7%SourceNot directly comparable
DeepSearchQA
GLM-5-Turbo—Kimi K2.577.1%SourceNot directly comparable
DeepPlanning
GLM-5-Turbo—Kimi K2.514.4%SourceNot directly comparable
Toolathlon
GLM-5-Turbo—Kimi K2.527.8%SourceNot directly comparable
MCP Atlas
GLM-5-Turbo—Kimi K2.529.5%SourceNot directly comparable
MCP-Tasks
GLM-5-Turbo—Kimi K2.559.1%SourceNot directly comparable
WideResearch
GLM-5-Turbo—Kimi K2.572.7%SourceNot directly comparable
Gert Labs
GLM-5-Turbo—Kimi K2.545.88%SourceNot directly comparable
ResearchClawBench
GLM-5-Turbo—Kimi K2.514.0%SourceNot directly comparable
JobBench
GLM-5-Turbo—Kimi K2.58.7%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-5-Turbo—Kimi K2.576.8%SourceNot directly comparable
SWE-bench Verified*
GLM-5-Turbo—Kimi K2.570.8%SourceNot directly comparable
LiveCodeBench v6
GLM-5-Turbo—Kimi K2.585.0%SourceNot directly comparable
SWE-bench Pro
GLM-5-Turbo—Kimi K2.550.7%SourceNot directly comparable
SWE Multilingual
GLM-5-Turbo—Kimi K2.573%SourceNot directly comparable
SWE-Rebench
GLM-5-Turbo—Kimi K2.558.5%SourceNot directly comparable
React Native Evals
GLM-5-Turbo—Kimi K2.577.2%SourceNot directly comparable
SciCode
GLM-5-Turbo—Kimi K2.548.7%SourceNot directly comparable
Reasoning
LongBench v2
GLM-5-Turbo—Kimi K2.561%SourceNot directly comparable
Knowledge
GPQA
GLM-5-Turbo—Kimi K2.587.6%SourceNot directly comparable
GPQA-D
GLM-5-Turbo—Kimi K2.587.6%SourceNot directly comparable
SuperGPQA
GLM-5-Turbo—Kimi K2.569.2%SourceNot directly comparable
MMLU-Pro
GLM-5-Turbo—Kimi K2.587.1%SourceNot directly comparable
MMLU-Pro (Arcee)
GLM-5-Turbo—Kimi K2.587.1%SourceNot directly comparable
HLE
GLM-5-Turbo—Kimi K2.530.1%SourceNot directly comparable
Math
AIME 2025
GLM-5-Turbo—Kimi K2.596.1%SourceNot directly comparable
AIME26
GLM-5-Turbo—Kimi K2.595.8%SourceNot directly comparable
AIME25 (Arcee)
GLM-5-Turbo—Kimi K2.596.3%SourceNot directly comparable
HMMT Feb 2025
GLM-5-Turbo—Kimi K2.595.4%SourceNot directly comparable
HMMT Nov 2025
GLM-5-Turbo—Kimi K2.591.1%SourceNot directly comparable
HMMT Feb 2026
GLM-5-Turbo—Kimi K2.587.1%SourceNot directly comparable
MMAnswerBench
GLM-5-Turbo—Kimi K2.581.8%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5-Turbo—Kimi K2.527.900%SourceNot directly comparable
FrontierMath v2 (Tier 4)
GLM-5-Turbo—Kimi K2.54.200%SourceNot directly comparable
Multilingual
Multimodal
Instruction following
IFEval
GLM-5-Turbo—Kimi K2.593.9%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5-Turbo or Kimi K2.5?
GLM-5-Turbo has the higher public score estimate, 61.64 versus 54.01, 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-Turbo or Kimi K2.5?
GLM-5-Turbo is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5-Turbo or Kimi K2.5?
GLM-5-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5-Turbo or Kimi K2.5?
For the stated presets, chat costs $0.0032 on GLM-5-Turbo and $0.0021 on Kimi K2.5; repository review costs $0.072 and $0.039; the cache-heavy agent loop costs $0.304 and $0.162. GLM-5-Turbo 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 K2.5 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 K2.5?
Kimi K2.5 has the larger documented context window: 256K, compared with 200K.
Related comparisons
Last updated September 10, 2026
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