Long documents
Prompts that approach the documented context limit
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the larger documented context window.
Confidence: documented
GLM-5.1 vs Kimi K2.5 (Reasoning)
Updated September 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- GLM-5.1 only
- 22
- Kimi K2.5 (Reasoning) only
- 5
- Like-for-like categories
- 0 / 8
Supported: GLM-5.1How the comparison works
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.
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (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 K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K2.5 (Reasoning) is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.5 (Reasoning) is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
51.5GLM-5.1—Kimi K2.5 (Reasoning)
Not comparable · BenchAlign v5.6
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
22 GLM-5.1 only
4 Shared
5 Kimi K2.5 (Reasoning) only
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.
Terminal-Bench 2.0Agentic
Normalized gap 12.7GLM-5.1:63.5%Kimi K2.5 (Reasoning):50.8%BrowseCompAgentic
Normalized gap 7.4GLM-5.1:68%Kimi K2.5 (Reasoning):60.6%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.6 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.
Agentic
Not comparable- GLM-5.1
- 42.1
- Estimated · #43/105
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- BenchAlign v5.6 lane · 9 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5.1
- 51.5
- Supported · #36/135
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- BenchAlign v5.6 lane · 7 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- 71.7
- Unranked · 2 rankable rows
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not ranked
- Kimi K2.5 (Reasoning)
- 63.3
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.1
- 50.2
- Supported · #54/160
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- BenchAlign v5.6 lane · 4 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not ranked
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- 92.4
- #4/124
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 63.8
- #3/7
- Kimi K2.5 (Reasoning)
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.1 | Kimi K2.5 (Reasoning) | Basis | Reading |
|---|---|---|---|---|
| Agentic | 42.1Estimated · #43/105 | Not ranked | Not comparableBenchAlign v5.6 lane · 9 vs 3 public rows | Not comparable |
| Coding | 51.5Supported · #36/135 | Not ranked | Not comparableBenchAlign v5.6 lane · 7 vs 2 public rows | Not comparable |
| Reasoning | 71.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 63.3Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 50.2Supported · #54/160 | Not ranked | Not comparableBenchAlign v5.6 lane · 4 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 92.4#4/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 63.8#3/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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 K2.5 (Reasoning)
- $0.0021
- Fits in one request
Kimi K2.5 (Reasoning) 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 K2.5 (Reasoning)
- $0.039
- Fits in one request
Kimi K2.5 (Reasoning) 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 K2.5 (Reasoning)
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
GLM-5.1 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 K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.1Kimi K2.5 (Reasoning)
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Kimi K2.5 (Reasoning)
256K
API model ID
GLM-5.1
Not sourced
Kimi K2.5 (Reasoning)
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 K2.5 (Reasoning)
Not published
Documented inputs
GLM-5.1
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Documented outputs
GLM-5.1
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Provider availability
GLM-5.1
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Reasoning profile
GLM-5.1
Reasoning
Kimi K2.5 (Reasoning)
Reasoning
Weight access
GLM-5.1
Open Weight
Kimi K2.5 (Reasoning)
Proprietary
License
GLM-5.1
Open Weight
Kimi K2.5 (Reasoning)
Proprietary
Release date
GLM-5.1
2026-04-07
Kimi K2.5 (Reasoning)
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0832 vs $0.039. Cache-heavy agent loop: $0.352 vs $0.162.
- Context tradeoff
- Kimi K2.5 (Reasoning) has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5.1 or Kimi K2.5 (Reasoning)?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5.1 or Kimi K2.5 (Reasoning)?
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.0021 on Kimi K2.5 (Reasoning); repository review costs $0.0832 and $0.039; the cache-heavy agent loop costs $0.352 and $0.162. GLM-5.1 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 K2.5 (Reasoning) 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 K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the larger documented context window: 256K, compared with 203K.
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 K2.5 (Reasoning)
API / mo$2,700
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 evidence31 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceKimi K2.5 (Reasoning)50.8%SourceGLM-5.1 leads this result
BrowseComp
GLM-5.168%SourceKimi K2.5 (Reasoning)60.6%SourceGLM-5.1 leads this result
τ³-bench results
GLM-5.170.6%SourceKimi K2.5 (Reasoning)—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
CyberGym
GLM-5.168.7%SourceKimi K2.5 (Reasoning)—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
Gert Labs
Shared sourceGLM-5.160.11%Kimi K2.5 (Reasoning)32.58%GLM-5.1 leads this result
ResearchClawBench
GLM-5.118.2%SourceKimi K2.5 (Reasoning)—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%SourceKimi K2.5 (Reasoning)—Not directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
NL2Repo
GLM-5.142.7%SourceKimi K2.5 (Reasoning)—Not directly comparable
SWE-Rebench
GLM-5.162.7%SourceKimi K2.5 (Reasoning)—Not directly comparable
Vibe Code Bench
Shared sourceGLM-5.131.46%Kimi K2.5 (Reasoning)17.54%GLM-5.1 leads this result
OpenHarmony Bench
GLM-5.152.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%SourceKimi K2.5 (Reasoning)—Not directly comparable
SWE-bench Verified
GLM-5.1—Kimi K2.5 (Reasoning)76.8%SourceNot directly comparable
Multimodal
MMMU-Pro
GLM-5.1—Kimi K2.5 (Reasoning)78.5%SourceNot directly comparable
Knowledge
GPQA-D
GLM-5.186.2%SourceKimi K2.5 (Reasoning)—Not directly comparable
HLE
GLM-5.152.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
GPQA Diamond (Vals)
GLM-5.184.5%SourceKimi K2.5 (Reasoning)—Not directly comparable
MMLU-Pro (Vals)
GLM-5.186.9%SourceKimi K2.5 (Reasoning)—Not directly comparable
GPQA
GLM-5.1—Kimi K2.5 (Reasoning)87.6%SourceNot directly comparable
MMLU-Pro
GLM-5.1—Kimi K2.5 (Reasoning)87.1%SourceNot directly comparable
Math
AIME26
GLM-5.195.3%SourceKimi K2.5 (Reasoning)—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceKimi K2.5 (Reasoning)—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceKimi K2.5 (Reasoning)—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceKimi K2.5 (Reasoning)—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceKimi K2.5 (Reasoning)—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceKimi K2.5 (Reasoning)—Not directly comparable
AIME 2025
GLM-5.1—Kimi K2.5 (Reasoning)96.1%SourceNot directly comparable
31 public results · 4 shared
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Last updated September 23, 2026