Model comparison
GPT-4.1 vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4.1 #111 (Supported); Kimi K2.5 (Reasoning) #63 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4.1 and Kimi K2.5 (Reasoning) share 16 comparable benchmark results. 2 of 8 categories are comparable. 4 results are unique to GPT-4.1; 11 to Kimi K2.5 (Reasoning).
Updated July 24, 2026- Shared results
- 16
- GPT-4.1 only
- 4
- Kimi K2.5 (Reasoning) only
- 11
- Comparable categories
- 2 / 8
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.5 (Reasoning) is clearly ahead on the BenchAlign aggregate, 58.61 to 50.44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in coding, where it averages 76.8 against 54.6. The single biggest benchmark swing on the page is SWE-bench Verified, 54.6% to 76.8%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). That is roughly 2.7x on output cost alone. Kimi K2.5 (Reasoning) is the reasoning model in the pair, while GPT-4.1 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. GPT-4.1 gives you the larger context window at 1M, 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 | GPT-4.1 | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Coding | GPT-4.154.6 | Margin→ 22.2 | Kimi K2.5 (Reasoning)76.8 |
| Knowledge | GPT-4.166.3 | Margin→ 20.9 | Kimi K2.5 (Reasoning)87.2 |
| Agentic | GPT-4.1Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)55.0 |
| Math | GPT-4.14.1 | MarginNo overlap | Kimi K2.5 (Reasoning)Not measured |
| Multimodal | GPT-4.1Not measured | MarginNo overlap | Kimi K2.5 (Reasoning)78.5 |
| Inst. Following | GPT-4.187.4 | 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 ↗
SWE-bench Verified
CodingA 54.6%B 76.8%Winner: Kimi K2.5 (Reasoning)Δ 22.2SWE-bench Verified: GPT-4.1 scored 54.6%; Kimi K2.5 (Reasoning) scored 76.8%. Kimi K2.5 (Reasoning) wins this benchmark. - Source ↗
GPQA
KnowledgeA 66.3%B 87.6%Winner: Kimi K2.5 (Reasoning)Δ 21.3GPQA: GPT-4.1 scored 66.3%; Kimi K2.5 (Reasoning) scored 87.6%. Kimi K2.5 (Reasoning) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4.1 | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4.1$2 input / $8 output | Kimi K2.5 (Reasoning)$0.6 input / $3 output | Kimi K2.5 (Reasoning) has the lower combined listed price. |
| Generation speedtokens per second | GPT-4.1108 tok/s | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4.11.02 s | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4.11M | Kimi K2.5 (Reasoning)128K | GPT-4.1 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GPT-4.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| τ²-bench resultsSource | 47.1% | 95.9% | Kimi K2.5 (Reasoning) leads |
| Gert LabsSource | 25.65% | 32.58% | Kimi K2.5 (Reasoning) leads |
| Terminal-Bench 2.0Source | — | 50.8% | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.1% | Not comparable |
| GDPval-AASource | — | 1003 | Not comparable |
CodingKimi K2.5 (Reasoning) wins4 benchmarks
Reasoning2 benchmarks
KnowledgeKimi K2.5 (Reasoning) wins9 benchmarks
| Benchmark | GPT-4.1 | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| MMLUSource | 90.2% | — | Not comparable |
| GPQASource | 66.3% | 87.6% | Kimi K2.5 (Reasoning) leads |
| Artificial Analysis Intelligence IndexSource | 19.4% | 35.4% | Kimi K2.5 (Reasoning) leads |
| AA-GPQA DiamondSource | 66.6% | 87.9% | Kimi K2.5 (Reasoning) leads |
| AA-HLESource | 4.6% | 29.4% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience IndexSource | -36.2% | -8.1% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience AccuracySource | 24.2% | 34.3% | Kimi K2.5 (Reasoning) leads |
| AA-Omniscience Hallucination RateSource | 79.6% | 64.6% | Kimi K2.5 (Reasoning) leads |
| MMLU-ProSource | — | 87.1% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Frequently Asked Questions (3)
Which is better, GPT-4.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) is ahead on BenchLM's BenchAlign leaderboard, 58.61 to 50.44. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 54.6% and 76.8%.
Which is better for knowledge tasks, GPT-4.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.2 versus 66.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for coding, GPT-4.1 or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 54.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.