Model comparison
GLM-4.6 vs Kimi K2.6
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.6 #85 (Supported); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.6 and Kimi K2.6 share 14 comparable benchmark results. 1 of 8 categories are comparable. 0 results are unique to GLM-4.6; 37 to Kimi K2.6.
Updated July 23, 2026- Shared results
- 14
- GLM-4.6 only
- 0
- Kimi K2.6 only
- 37
- Comparable categories
- 1 / 8
Pick Kimi K2.6 if you want the stronger benchmark profile. GLM-4.6 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.6 has the cleaner BenchAlign overall profile here, landing at 56.79 versus 55.12. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Kimi K2.6's sharpest advantage is in mathematics, where it averages 67.1 against 3.4. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 3.819% to 38.966%.
Kimi K2.6 gives you the larger context window at 256K, compared with 200K for GLM-4.6.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GLM-4.6 | Δ | Kimi K2.6 |
|---|---|---|---|
| Math | GLM-4.63.4 | Margin→ 63.7 | Kimi K2.667.1 |
| Agentic | GLM-4.6Not measured | MarginNo overlap | Kimi K2.673.5 |
| Coding | GLM-4.6Not measured | MarginNo overlap | Kimi K2.664.4 |
| Knowledge | GLM-4.6Not measured | MarginNo overlap | Kimi K2.642.2 |
| Multimodal | GLM-4.6Not measured | MarginNo overlap | Kimi K2.679.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 3.819%B 38.966%Winner: Kimi K2.6Δ 35.1FrontierMath v2 (Tiers 1-3): GLM-4.6 scored 3.819%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark. - Source ↗
FrontierMath v2 (Tier 4)
MathA 2.128%B 14.580%Winner: Kimi K2.6Δ 12.5FrontierMath v2 (Tier 4): GLM-4.6 scored 2.128%; Kimi K2.6 scored 14.580%. Kimi K2.6 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.6 | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.6Not available | Kimi K2.6$0.95 input / $4 output | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-4.6Not available | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.6Not available | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.6200K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | GLM-4.6 | Kimi K2.6 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 76.9% | 95.9% | Kimi K2.6 leads |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| BrowseCompSource | — | 83.2% | Not comparable |
| OSWorld-VerifiedSource | — | 73.1% | Not comparable |
| ToolathlonSource | — | 50% | Not comparable |
| MCP AtlasSource | — | 55.9% | Not comparable |
| Claw-EvalSource | — | 62.3% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| AA Agentic IndexSource | — | 30.3% | Not comparable |
| GDPval-AASource | — | 34.5% | Not comparable |
| GDPval-AASource | — | 1189 | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| Gert LabsSource | — | 56.82% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| terminalBenchHardSource | — | 43.9% | Not comparable |
Coding10 benchmarks
| Benchmark | GLM-4.6 | Kimi K2.6 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 3.09% | 37.89% | Kimi K2.6 leads |
| AA-SciCodeSource | 33.1% | 53.5% | Kimi K2.6 leads |
| SWE-bench VerifiedSource | — | 80.2% | Not comparable |
| LiveCodeBench v6Source | — | 89.6% | Not comparable |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| SWE MultilingualSource | — | 76.7% | Not comparable |
| SciCodeSource | — | 52.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| cursorBench31Source | — | 47.6% | Not comparable |
| AA Coding IndexSource | — | 61.8% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.6 | Kimi K2.6 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 23.0% | 44.2% | Kimi K2.6 leads |
| AA-GPQA DiamondSource | 63.2% | 91.1% | Kimi K2.6 leads |
| AA-HLESource | 5.2% | 35.9% | Kimi K2.6 leads |
| AA-Omniscience IndexSource | -31.6% | 6.4% | Kimi K2.6 leads |
| AA-Omniscience AccuracySource | 20.8% | 32.8% | Kimi K2.6 leads |
| AA-Omniscience Hallucination RateSource | 66.1% | 39.3% | Kimi K2.6 leads |
| GPQASource | — | 90.5% | Not comparable |
| GPQA-DSource | — | 90.5% | Not comparable |
| HLESource | — | 34.7% | Not comparable |
MathKimi K2.6 wins5 benchmarks
Multimodal7 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-4.6 | Kimi K2.6 | Result |
|---|---|---|---|
| AA-IFBenchSource | 36.7% | 76.0% | Kimi K2.6 leads |
Frequently Asked Questions (2)
Which is better, GLM-4.6 or Kimi K2.6?
Kimi K2.6 is ahead on BenchLM's BenchAlign leaderboard, 56.79 to 55.12. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 3.819% and 38.966%.
Which is better for math, GLM-4.6 or Kimi K2.6?
Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 3.4. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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