Model comparison
GPT-5.3 Codex vs Kimi K2.5 (Reasoning)
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); Kimi K2.5 (Reasoning) #57 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Kimi K2.5 (Reasoning) share 17 comparable benchmark results. 2 of 8 categories are comparable. 4 results are unique to GPT-5.3 Codex; 10 to Kimi K2.5 (Reasoning).
Updated July 20, 2026- Shared results
- 17
- GPT-5.3 Codex only
- 4
- Kimi K2.5 (Reasoning) only
- 10
- Comparable categories
- 2 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Kimi K2.5 (Reasoning) only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 17 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
GPT-5.3 Codex is clearly ahead on the BenchAlign aggregate, 66.69 to 59.35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in agentic, where it averages 71.4 against 55. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77.3% to 50.8%. Kimi K2.5 (Reasoning) does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5 (Reasoning). That is roughly 4.7x on output cost alone. GPT-5.3 Codex gives you the larger context window at 400K, 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-5.3 Codex | Δ | Kimi K2.5 (Reasoning) |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin← 16.4 | Kimi K2.5 (Reasoning)55.0 |
| Coding | GPT-5.3 Codex67.2 | Margin→ 9.6 | Kimi K2.5 (Reasoning)76.8 |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | Kimi K2.5 (Reasoning)87.2 |
| Multimodal | GPT-5.3 CodexNot 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 77.3%B 50.8%Winner: GPT-5.3 CodexΔ 26.5Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; Kimi K2.5 (Reasoning) scored 50.8%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 85%B 76.8%Winner: GPT-5.3 CodexΔ 8.2SWE-bench Verified: GPT-5.3 Codex scored 85%; Kimi K2.5 (Reasoning) scored 76.8%. GPT-5.3 Codex wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 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-5.3 Codex79 tok/s | Kimi K2.5 (Reasoning)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Kimi K2.5 (Reasoning)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Kimi K2.5 (Reasoning)128K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.3 Codex wins10 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | 50.8% | GPT-5.3 Codex leads |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 95.9% | Kimi K2.5 (Reasoning) leads |
| Gert LabsSource | 57.47% | 32.58% | GPT-5.3 Codex leads |
| JobBenchSource | 33.7% | — | Not comparable |
| BrowseCompSource | — | 60.6% | Not comparable |
| APEX-Agents-AASource | — | 11.5% | Not comparable |
| AA Agentic IndexSource | — | 21.7% | Not comparable |
| GDPval-AASource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 1009 | Not comparable |
CodingKimi K2.5 (Reasoning) wins6 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | 76.8% | GPT-5.3 Codex leads |
| SWE-bench ProSource | 56.8% | — | Not comparable |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | 17.54% | GPT-5.3 Codex leads |
| AA-SciCodeSource | 53.2% | 49.0% | GPT-5.3 Codex leads |
| AA Coding IndexSource | — | 46.8% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 35.4% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 87.9% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 29.4% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | -8.1% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 34.3% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 64.6% | Kimi K2.5 (Reasoning) leads |
| GPQASource | — | 87.6% | Not comparable |
| MMLU-ProSource | — | 87.1% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AIME 2025Source | — | 96.1% | Not comparable |
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K2.5 (Reasoning) | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | 70.2% | GPT-5.3 Codex leads |
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or Kimi K2.5 (Reasoning)?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 59.35. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77.3% and 50.8%.
Which is better for coding, GPT-5.3 Codex or Kimi K2.5 (Reasoning)?
Kimi K2.5 (Reasoning) has the edge for coding in this comparison, averaging 76.8 versus 67.2. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.3 Codex or Kimi K2.5 (Reasoning)?
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 55. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.