Model comparison
GLM-5.1 vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5.1 #18 (Supported); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5.1 and Kimi K2.5 (Reasoning) share 20 comparable benchmark results. 3 of 8 categories are comparable. 16 results are unique to GLM-5.1; 7 to Kimi K2.5 (Reasoning).
Updated July 23, 2026- Shared results
- 20
- GLM-5.1 only
- 16
- Kimi K2.5 (Reasoning) only
- 7
- Comparable categories
- 3 / 8
Pick GLM-5.1 if you want the stronger benchmark profile. Kimi K2.5 (Reasoning) only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 20 shared benchmark results across 6 evidence categories; 3 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 59.35. 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 agentic, where it averages 65.4 against 55. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 50.8%. Kimi K2.5 (Reasoning) does hit back in knowledge, 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 $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). GLM-5.1 gives you the larger context window at 203K, compared with 128K for Kimi K2.5 (Reasoning).
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 K2.5 (Reasoning) |
|---|---|---|---|
| Knowledge | GLM-5.152.3 | Margin→ 34.9 | Kimi K2.5 (Reasoning)87.2 |
| Coding | GLM-5.161.3 | Margin→ 15.5 | Kimi K2.5 (Reasoning)76.8 |
| Agentic | GLM-5.165.4 | Margin← 10.4 | Kimi K2.5 (Reasoning)55.0 |
| Math | GLM-5.162.0 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | GLM-5.1Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 63.5%B 50.8%Winner: GLM-5.1Δ 12.7Terminal-Bench 2.0: GLM-5.1 scored 63.5%; Kimi K2.5 (Reasoning) scored 50.8%. GLM-5.1 wins this benchmark. - Source ↗
BrowseComp
AgenticA 68%B 60.6%Winner: GLM-5.1Δ 7.4BrowseComp: GLM-5.1 scored 68%; Kimi K2.5 (Reasoning) scored 60.6%. GLM-5.1 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 K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5.1$1.4 input / $4.4 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | Kimi K2.5 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | GLM-5.1Not available | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-5.1Not available | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5.1203K | Kimi K2.5 (Reasoning)128K | GLM-5.1 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5.1 wins13 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 63.5% | 50.8% | GLM-5.1 leads |
| BrowseCompSource | 68% | 60.6% | GLM-5.1 leads |
| τ³-bench resultsSource | 70.6% | — | Not comparable |
| MCP AtlasSource | 71.8% | — | Not comparable |
| CyberGymSource | 68.7% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| AA Agentic IndexSource | 29.9% | 21.7% | GLM-5.1 leads |
| τ²-bench resultsSource | 97.7% | 95.9% | GLM-5.1 leads |
| GDPval-AASource | 37.8% | 25.4% | GLM-5.1 leads |
| Gert LabsSource | 60.11% | 32.58% | GLM-5.1 leads |
| GDPval-AASource | 1257 | 1009 | GLM-5.1 leads |
| ResearchClawBenchSource | 18.2% | — | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
CodingKimi K2.5 (Reasoning) wins7 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.4% | — | Not comparable |
| NL2RepoSource | 42.7% | — | Not comparable |
| SWE-RebenchSource | 62.7% | — | Not comparable |
| Vibe Code BenchSource | 31.46% | 17.54% | GLM-5.1 leads |
| AA Coding IndexSource | 55.8% | 46.8% | GLM-5.1 leads |
| AA-SciCodeSource | 43.8% | 49.0% | Kimi K2.5 (Reasoning) leads |
| SWE-bench VerifiedSource | — | 76.8% | Not comparable |
Reasoning2 benchmarks
KnowledgeKimi K2.5 (Reasoning) wins10 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| GPQA-DSource | 86.2% | — | Not comparable |
| HLESource | 52.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 40.2% | 35.4% | GLM-5.1 leads |
| AA-GPQA DiamondSource | 86.8% | 87.9% | Kimi K2.5 (Reasoning) leads |
| AA-HLESource | 28.0% | 29.4% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience IndexSource | 1.9% | -8.1% | GLM-5.1 leads |
| AA-Omniscience AccuracySource | 24.2% | 34.3% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience Hallucination RateSource | 29.4% | 64.6% | GLM-5.1 leads |
| GPQASource | — | 87.6% | Not comparable |
| MMLU-ProSource | — | 87.1% | Not comparable |
Math7 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AIME26Source | 95.3% | — | Not comparable |
| HMMT Nov 2025Source | 94.0% | — | Not comparable |
| HMMT Feb 2026Source | 82.6% | — | Not comparable |
| MMAnswerBenchSource | 83.8% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 33.448% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 12.500% | — | Not comparable |
| AIME 2025Source | — | 96.1% | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | GLM-5.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | 76.3% | 70.2% | GLM-5.1 leads |
Frequently Asked Questions (4)
Which is better, GLM-5.1 or Kimi K2.5 (Reasoning)?
GLM-5.1 is ahead on BenchLM's BenchAlign leaderboard, 67.74 to 59.35. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 50.8%.
Which is better for knowledge tasks, GLM-5.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 61.3. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5.1 or Kimi K2.5 (Reasoning)?
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.4 versus 55. 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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