Coding
Like-for-like- Kimi K2.6
- 64.4
- Muse Glimmer 30B
- 57.8
- Weighted basis
- 3 vs 3 rows
- Reading
- Kimi K2.6 leads
Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.
See RadarModel comparison
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
9 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.
Code generation, repair, and software-engineering tasks
Kimi K2.6
Kimi K2.6 leads on the same 3 weighted benchmark rows.
Confidence: stronger
Prompts that approach the documented context limit
Kimi K2.6
Kimi K2.6 has the larger documented context window.
Confidence: documented
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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. Muse Glimmer 30B does not fit this workload in one request. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate. Muse Glimmer 30B 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.
2 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 K2.6 | Muse Glimmer 30B | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 64.4 | 57.8 | Like-for-like3 vs 3 rows | Kimi K2.6 leads |
| Multimodal | 79.8 | 75.7 | Like-for-like2 vs 2 rows | Kimi K2.6 leads |
| Agentic | 73.5 | 65.9 | Directional only3 vs 1 rows | Directional only |
| Math | 67.1 | 94.7 | Directional only4 vs 1 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 42.2 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 77.0 | Not comparable0 vs 1 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.
SciCode
Coding
SWE-bench Pro
Coding
OSWorld-Verified
Agentic
MMMU-Pro
Multimodal
SWE-bench Verified
Coding
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
Muse Glimmer 30B has no comparable published API token rate.
50K fresh input + 3K output tokens
Muse Glimmer 30B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Muse Glimmer 30B does not fit this workload in one request. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate. Muse Glimmer 30B 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 K2.6
256K
Muse Glimmer 30B
131K
Kimi K2.6
Not sourced
Muse Glimmer 30B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.6
Not published
Muse Glimmer 30B
No comparable hosted API rate
Kimi K2.6
Not sourced
Muse Glimmer 30B
Not sourced
Kimi K2.6
Not sourced
Muse Glimmer 30B
Not sourced
Kimi K2.6
Not sourced
Muse Glimmer 30B
Not sourced
Kimi K2.6
Reasoning
Muse Glimmer 30B
Reasoning
Kimi K2.6
Open Weight
Muse Glimmer 30B
Open Weight
Kimi K2.6
Open Weight
Muse Glimmer 30B
Open Weight
Kimi K2.6
2026-04-20
Muse Glimmer 30B
2026-08-10
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
OSWorld-Verified
Kimi K2.6 leads this result
Toolathlon
Not directly comparable
MCP Atlas
Muse Glimmer 30B leads this result
Claw-Eval
Not directly comparable
DeepSearchQA
Kimi K2.6 leads this result
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
skillsBench
Not directly comparable
SWE-bench Verified
Kimi K2.6 leads this result
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Kimi K2.6 leads this result
SWE Multilingual
Not directly comparable
SciCode
Kimi K2.6 leads this result
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
AIME26
Kimi K2.6 leads this result
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
MMMU-Pro
Kimi K2.6 leads this result
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Kimi K2.6 leads this result
MathVision
Not directly comparable
V*
Not directly comparable
ScreenSpot Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
IFBench
Not directly comparable
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.
Kimi K2.6 leads the like-for-like coding comparison across 3 shared weighted benchmark rows.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Kimi K2.6 has the larger documented context window: 256K, compared with 131K.
Last updated August 10, 2026
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