Model comparison
GPT-5.3 Codex vs Kimi K3
Head-to-head evidence from 13 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); Kimi K3 #4 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Kimi K3 share 13 comparable benchmark results. 1 of 8 categories are comparable. 8 results are unique to GPT-5.3 Codex; 44 to Kimi K3.
Updated July 23, 2026- Shared results
- 13
- GPT-5.3 Codex only
- 8
- Kimi K3 only
- 44
- Comparable categories
- 1 / 8
Pick Kimi K3 if you want the stronger benchmark profile. GPT-5.3 Codex only becomes the better choice if you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K3 is clearly ahead on the BenchAlign aggregate, 80.96 to 66.69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K3's sharpest advantage is in agentic, where it averages 89.5 against 71.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77.3% to 88.3%.
Kimi K3 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $1.75 input / $14.00 output per 1M tokens for GPT-5.3 Codex. Kimi K3 gives you the larger context window at 1.05M, compared with 400K for GPT-5.3 Codex.
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 K3 |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin→ 18.1 | Kimi K389.5 |
| Coding | GPT-5.3 Codex67.2 | MarginNo overlap | Kimi K3Not measured |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | Kimi K361.0 |
| Multimodal | GPT-5.3 CodexNot measured | MarginNo overlap | Kimi K378.5 |
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 88.3%Winner: Kimi K3Δ 11Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; Kimi K3 scored 88.3%. Kimi K3 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 K3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Kimi K3$3 input / $15 output | GPT-5.3 Codex has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Kimi K3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Kimi K3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Kimi K31.05M | Kimi K3 lists the larger context window. |
Benchmark Deep Dive
AgenticKimi K3 wins24 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K3 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | 88.3% | Kimi K3 leads |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | — | Not comparable |
| Gert LabsSource | 57.47% | — | Not comparable |
| JobBenchSource | 33.7% | 52.9% | Kimi K3 leads |
| BrowseCompSource | — | 91.2% | Not comparable |
| DeepSearchQASource | — | 95.0% | Not comparable |
| Toolathlon-VerifiedSource | — | 73.2% | Not comparable |
| MCP AtlasSource | — | 84.2% | Not comparable |
| AutomationBenchSource | — | 30.8% | Not comparable |
| APEX-AgentsSource | — | 37.6% | Not comparable |
| SpreadsheetBench 2Source | — | 34.8% | Not comparable |
| DECK-BenchSource | — | 73.5% | Not comparable |
| AA Agentic IndexSource | — | 50.1% | Not comparable |
| GDPval-AASource | — | 59.0% | Not comparable |
| GDPval-AASource | — | 1679 | Not comparable |
| AA BriefcaseSource | — | 1543 | Not comparable |
| AA AutomationBenchSource | — | 52.7% | Not comparable |
| AA EnterpriseOps-GymSource | — | 45.3% | Not comparable |
| AA Harvey LABSource | — | 94.6% | Not comparable |
| AA Tau3 BankingSource | — | 33.4% | Not comparable |
| aaTerminalBench21Source | — | 85% | Not comparable |
| APEX-Agents-AASource | — | 41.3% | Not comparable |
| AA ITBenchSource | — | 47.7% | Not comparable |
Coding13 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K3 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | — | Not comparable |
| SWE-bench ProSource | 56.8% | — | Not comparable |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 58.7% | Kimi K3 leads |
| deepSweSource | — | 67.5% | Not comparable |
| FrontierSWESource | — | 81.2% | Not comparable |
| ProgramBenchSource | — | 77.8% | Not comparable |
| Kimi Code Bench v2Source | — | 72.9% | Not comparable |
| sweMarathonSource | — | 42% | Not comparable |
| PostTrain BenchSource | — | 36.6% | Not comparable |
| MLS-Bench LiteSource | — | 48.3% | Not comparable |
| AA Coding IndexSource | — | 76.2% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 57.1% | Kimi K3 leads |
| AA-GPQA DiamondSource | 91.5% | 93.5% | Kimi K3 leads |
| AA-HLESource | 39.9% | 44.3% | Kimi K3 leads |
| AA-Omniscience IndexSource | 9.9% | 18.4% | Kimi K3 leads |
| AA-Omniscience AccuracySource | 51.8% | 46.0% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 50.9% | Kimi K3 leads |
| GPQASource | — | 93.5% | Not comparable |
| GPQA-DSource | — | 93.5% | Not comparable |
| HLESource | — | 56% | Not comparable |
| HLE w/o toolsSource | — | 43.5% | Not comparable |
Multimodal15 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 78.5% | 80.5% | Kimi K3 leads |
| Design Arena WebsiteSource | 1193 | 1386 | Kimi K3 leads |
| OfficeQA ProSource | — | 63.3% | Not comparable |
| MMMU-ProSource | — | 81.6% | Not comparable |
| MMMU-Pro w/ PythonSource | — | 83.4% | Not comparable |
| CharXiv w/o toolsSource | — | 84.8% | Not comparable |
| CharXivSource | — | 91.3% | Not comparable |
| MathVisionSource | — | 94.3% | Not comparable |
| MathVision w/ PythonSource | — | 97.8% | Not comparable |
| BabyVision w/ PythonSource | — | 85.7% | Not comparable |
| ZeroBenchSource | — | 23.0% | Not comparable |
| ZeroBench w/ PythonSource | — | 41.0% | Not comparable |
| WorldVQA ForceAnswerSource | — | 51.0% | Not comparable |
| OmniDocBenchSource | — | 91.1% | Not comparable |
| PerceptionBenchSource | — | 58.5% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | Kimi K3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | — | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.3 Codex or Kimi K3?
Kimi K3 is ahead on BenchLM's BenchAlign leaderboard, 80.96 to 66.69. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77.3% and 88.3%.
Which is better for agentic tasks, GPT-5.3 Codex or Kimi K3?
Kimi K3 has the edge for agentic tasks in this comparison, averaging 89.5 versus 71.4. Inside this category, JobBench 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.