Model comparison
GLM-5 vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (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 and Kimi K2.5 (Reasoning) share 18 comparable benchmark results. 3 of 8 categories are comparable. 31 results are unique to GLM-5; 9 to Kimi K2.5 (Reasoning).
Updated July 22, 2026- Shared results
- 18
- GLM-5 only
- 31
- Kimi K2.5 (Reasoning) only
- 9
- Comparable categories
- 3 / 8
Pick GLM-5 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 18 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 is clearly ahead on the BenchAlign aggregate, 66.06 to 59.35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 55. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% 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 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). Kimi K2.5 (Reasoning) is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GLM-5 gives you the larger context window at 200K, 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 | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Knowledge | GLM-566.4 | Margin→ 20.8 | Kimi K2.5 (Reasoning)87.2 |
| Coding | GLM-566.3 | Margin→ 10.5 | Kimi K2.5 (Reasoning)76.8 |
| Agentic | GLM-556.2 | Margin← 1.2 | Kimi K2.5 (Reasoning)55.0 |
| Reasoning | GLM-560.8 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Math | GLM-556.3 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | GLM-5Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
| Inst. Following | GLM-592.6 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 56.2%B 50.8%Winner: GLM-5Δ 5.4Terminal-Bench 2.0: GLM-5 scored 56.2%; Kimi K2.5 (Reasoning) scored 50.8%. GLM-5 wins this benchmark. - Source ↗
GPQA
KnowledgeA 86%B 87.6%Winner: Kimi K2.5 (Reasoning)Δ 1.6GPQA: GLM-5 scored 86%; Kimi K2.5 (Reasoning) scored 87.6%. Kimi K2.5 (Reasoning) wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85.7%B 87.1%Winner: Kimi K2.5 (Reasoning)Δ 1.4MMLU-Pro: GLM-5 scored 85.7%; Kimi K2.5 (Reasoning) scored 87.1%. Kimi K2.5 (Reasoning) wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 77.8%B 76.8%Winner: GLM-5Δ 1SWE-bench Verified: GLM-5 scored 77.8%; Kimi K2.5 (Reasoning) scored 76.8%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 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-574 tok/s | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | Kimi K2.5 (Reasoning)128K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
AgenticGLM-5 wins17 benchmarks
| Benchmark | GLM-5 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | 50.8% | GLM-5 leads |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 95.9% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | 11.5% | GLM-5 leads |
| Gert LabsSource | 50.99% | 32.58% | GLM-5 leads |
| BrowseCompSource | — | 60.6% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
CodingKimi K2.5 (Reasoning) wins9 benchmarks
| Benchmark | GLM-5 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | 76.8% | GLM-5 leads |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | — | Not comparable |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 49.0% | Kimi K2.5 (Reasoning) leads |
| Vibe Code BenchSource | — | 17.54% | Not comparable |
| AA Coding IndexSource | — | 46.8% | Not comparable |
Reasoning4 benchmarks
KnowledgeKimi K2.5 (Reasoning) wins12 benchmarks
| Benchmark | GLM-5 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| GPQASource | 86% | 87.6% | Kimi K2.5 (Reasoning) leads |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | 87.1% | Kimi K2.5 (Reasoning) leads |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | 35.4% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 87.9% | Kimi K2.5 (Reasoning) leads |
| AA-HLESource | 27.2% | 29.4% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience IndexSource | 2.0% | -8.1% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 34.3% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 64.6% | GLM-5 leads |
Math9 benchmarks
| Benchmark | GLM-5 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| AIME 2025Source | — | 96.1% | Not comparable |
Multilingual2 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-5 or Kimi K2.5 (Reasoning)?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 59.35. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 50.8%.
Which is better for knowledge tasks, GLM-5 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 66.4. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-5 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 66.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-5 or Kimi K2.5 (Reasoning)?
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 55. Inside this category, Gert Labs is the benchmark that creates the most daylight between them.
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