Model comparison
GLM-5.1 vs Kimi K2.6
Head-to-head evidence from 31 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); Kimi K2.6 #74 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and Kimi K2.6 share 31 comparable benchmark results. 4 of 8 categories are comparable. 5 results are unique to GLM-5.1; 20 to Kimi K2.6.
Updated July 23, 2026- Shared results
- 31
- GLM-5.1 only
- 5
- Kimi K2.6 only
- 20
- Comparable categories
- 4 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. Kimi K2.6 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 31 shared benchmark results across 7 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-5.1 is clearly ahead on the BenchAlign aggregate, 67.74 to 56.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in knowledge, where it averages 52.3 against 42.2. The single biggest benchmark swing on the page is HLE, 52.3% to 34.7%. Kimi K2.6 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. Kimi K2.6 gives you the larger context window at 256K, compared with 203K for GLM-5.1.
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-5.1 | Δ | Kimi K2.6 |
|---|---|---|---|
| Knowledge | GLM-5.152.3 | Margin← 10.1 | Kimi K2.642.2 |
| Agentic | GLM-5.165.4 | Margin→ 8.1 | Kimi K2.673.5 |
| Math | GLM-5.162.0 | Margin→ 5.1 | Kimi K2.667.1 |
| Coding | GLM-5.161.3 | Margin→ 3.1 | Kimi K2.664.4 |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | Kimi K2.679.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 52.3%B 34.7%Winner: GLM-5.1Δ 17.6HLE: GLM-5.1 scored 52.3%; Kimi K2.6 scored 34.7%. GLM-5.1 wins this benchmark. - Source ↗
BrowseComp
AgenticA 68%B 83.2%Winner: Kimi K2.6Δ 15.2BrowseComp: GLM-5.1 scored 68%; Kimi K2.6 scored 83.2%. Kimi K2.6 wins this benchmark. - Source ↗
HMMT Feb 2026
MathA 82.6%B 92.7%Winner: Kimi K2.6Δ 10.1HMMT Feb 2026: GLM-5.1 scored 82.6%; Kimi K2.6 scored 92.7%. Kimi K2.6 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 33.448%B 38.966%Winner: Kimi K2.6Δ 5.5FrontierMath v2 (Tiers 1-3): GLM-5.1 scored 33.448%; Kimi K2.6 scored 38.966%. Kimi K2.6 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 66.7%Winner: Kimi K2.6Δ 3.2Terminal-Bench 2.0: GLM-5.1 scored 63.5%; Kimi K2.6 scored 66.7%. 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-5.1 | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Kimi K2.6$0.95 input / $4 output | Kimi K2.6 has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
AgenticKimi K2.6 wins19 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.6 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 66.7% | Kimi K2.6 leads |
| BrowseCompSource | 68% | 83.2% | Kimi K2.6 leads |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | 55.9% | GLM-5.1 leads |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | 62.3% | Tie |
| AA Agentic IndexSource | 29.9% | 30.3% | Kimi K2.6 leads |
| τ²-bench resultsSource | 97.7% | 95.9% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | 34.5% | GLM-5.1 leads |
| Gert LabsSource | 60.11% | 56.82% | GLM-5.1 leads |
| GDPval-AASource | 1257 | 1189 | GLM-5.1 leads |
| ResearchClawBenchSource | 18.2% | 18.0% | GLM-5.1 leads |
| OSWorld-VerifiedSource | — | 73.1% | Not comparable |
| ToolathlonSource | — | 50% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| terminalBenchHardSource | — | 43.9% | Not comparable |
CodingKimi K2.6 wins12 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.6 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | 58.6% | Kimi K2.6 leads |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | 37.89% | Kimi K2.6 leads |
| AA Coding IndexSource | 55.8% | 61.8% | Kimi K2.6 leads |
| AA-SciCodeSource | 43.8% | 53.5% | Kimi K2.6 leads |
| SWE-bench VerifiedSource | — | 80.2% | Not comparable |
| LiveCodeBench v6Source | — | 89.6% | 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 |
Reasoning2 benchmarks
KnowledgeGLM-5.1 wins9 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.6 | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | 90.5% | Kimi K2.6 leads |
| HLESource | 52.3% | 34.7% | GLM-5.1 leads |
| Artificial Analysis Intelligence IndexSource | 40.2% | 44.2% | Kimi K2.6 leads |
| AA-GPQA DiamondSource | 86.8% | 91.1% | Kimi K2.6 leads |
| AA-HLESource | 28.0% | 35.9% | Kimi K2.6 leads |
| AA-Omniscience IndexSource | 1.9% | 6.4% | Kimi K2.6 leads |
| AA-Omniscience AccuracySource | 24.2% | 32.8% | Kimi K2.6 leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 39.3% | GLM-5.1 leads |
| GPQASource | — | 90.5% | Not comparable |
MathKimi K2.6 wins6 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.6 | Result |
|---|---|---|---|
| AIME26Source | 95.3% | 96.4% | Kimi K2.6 leads |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | 92.7% | Kimi K2.6 leads |
| MMAnswerBenchSource | 83.8% | 86.0% | Kimi K2.6 leads |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | 38.966% | Kimi K2.6 leads |
| FrontierMath v2 (Tier 4)Source | 12.500% | 14.580% | Kimi K2.6 leads |
Multimodal7 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.6 | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 76.0% | GLM-5.1 leads |
Frequently Asked Questions (5)
Which is better, GLM-5.1 or Kimi K2.6?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 56.79. The biggest single separator in this matchup is HLE, where the scores are 52.3% and 34.7%.
Which is better for knowledge tasks, GLM-5.1 or Kimi K2.6?
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 42.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or Kimi K2.6?
Kimi K2.6 has the edge for coding in this comparison, averaging 64.4 versus 61.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5.1 or Kimi K2.6?
Kimi K2.6 has the edge for math in this comparison, averaging 67.1 versus 62. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or Kimi K2.6?
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.5 versus 65.4. Inside this category, GDPval-AA 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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