Knowledge
Directional only- Kimi K2.5
- 56.9
- ZAYA1-8B
- 73.6
- Weighted basis
- 4 vs 2 rows
- Reading
- Directional only
Model comparison
Updated August 5, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Kimi K2.5 has the higher public score, 58.83 versus 31.58, and the 90% score intervals do not overlap.
7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
Kimi K2.5
Kimi K2.5 has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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. ZAYA1-8B does not fit this workload in one request. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. ZAYA1-8B has no comparable published API token rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Kimi K2.5 | ZAYA1-8B | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 56.9 | 73.6 | Directional only4 vs 2 rows | Directional only |
| Math | 60.6 | 80.4 | Directional only4 vs 2 rows | Directional only |
| Instruction following | 93.9 | 64.1 | Directional only1 vs 2 rows | Directional only |
| Agentic | 55.0 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 59.4 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Reasoning | 61.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | 82.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multimodal | 78.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
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.
GPQA
Knowledge
HMMT Feb 2026
Math
MMLU-Pro
Knowledge
IFEval
Instruction following
AIME26
Math
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.
1K fresh input + 500 output tokens
ZAYA1-8B has no comparable published API token rate.
50K fresh input + 3K output tokens
ZAYA1-8B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
ZAYA1-8B does not fit this workload in one request. Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. ZAYA1-8B has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Kimi K2.5
256K
ZAYA1-8B
131K
Kimi K2.5
Not sourced
ZAYA1-8B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.5
Not published
ZAYA1-8B
No comparable hosted API rate
Kimi K2.5
Not sourced
ZAYA1-8B
Not sourced
Kimi K2.5
Not sourced
ZAYA1-8B
Not sourced
Kimi K2.5
Not sourced
ZAYA1-8B
Not sourced
Kimi K2.5
Non-Reasoning
ZAYA1-8B
Reasoning
Kimi K2.5
Open Weight
ZAYA1-8B
Open Weight
Kimi K2.5
Open Weight
ZAYA1-8B
Open Weight
Kimi K2.5
2026-02-01
ZAYA1-8B
2026-05-05
Run the same representative tasks against both endpoints before changing production traffic.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Not directly comparable
DeepSearchQA
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
BFCL v4
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Verified*
Not directly comparable
LiveCodeBench v6
Kimi K2.5 leads this result
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
SciCode
Not directly comparable
LongBench v2
Not directly comparable
GPQA
Kimi K2.5 leads this result
GPQA-D
Kimi K2.5 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Kimi K2.5 leads this result
MMLU-Pro (Arcee)
Not directly comparable
HLE
Not directly comparable
AIME 2025
Not directly comparable
AIME26
Kimi K2.5 leads this result
AIME25 (Arcee)
Not directly comparable
HMMT Feb 2025
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Kimi K2.5 leads this result
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Kimi K2.5 has the higher public score, 58.83 versus 31.58, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Kimi K2.5 has the larger documented context window: 256K, compared with 131K.
Last updated August 5, 2026
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