GPT-5.3-Codex vs Kimi K3.1 Pro
Full profiles: GPT-5.3-Codex (OpenAI) and Kimi K3.1 Pro (Moonshot AI): every benchmark, price and rank.
Updated · Data from 6 sources
As of 24 Oct 2026, GPT-5.3-Codex (xhigh) and Kimi K3.1 Pro are neck and neck on quality (63.8 vs 66.8 on the BenchLeader Index, inside the ±7.9 margin of error), and Kimi K3.1 Pro costs 1.2× less, which makes it the better buy for most workloads. Pick GPT-5.3-Codex for agents & tools (80 vs 67); pick Kimi K3.1 Pro for reasoning (81 vs 69).
GPT-5.3-Codex benchmarks xhigh OpenAI
Kimi K3.1 Pro benchmarks Moonshot AIOpen in the comparison toolWhich to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Agents & tool use | GPT-5.3-Codex | 80 vs 67 category score | High |
| Reasoning | Kimi K3.1 Pro | 81 vs 69 category score; CritPt 24.6% vs 16.9% | High |
| Knowledge | Kimi K3.1 Pro | 75 vs 66 category score; AA-Omniscience 29.5 vs 10.9 | High |
| Long documents | Kimi K3.1 Pro | 68 vs 67 category score; AA-LCR 86.3% vs 83.3% | Medium |
| Images and other media | Kimi K3.1 Pro | 67 vs 62 category score; MMMU-Pro 83.9% vs 78.5% | Medium |
| Lowest cost | Kimi K3.1 Pro | $4.00 vs $4.81 per 1M blended; agentic coding session $0.160 vs $0.161 | – |
| Fastest first answer | Kimi K3.1 Pro | 27.7 s (Kimi K3.1 Pro) vs 60.6 s (GPT-5.3-Codex (xhigh)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-5.3-Codex (xhigh) and Kimi K3.1 Pro are neck and neck on quality (63.8 vs 66.8, within the ±7.9 margin).
- GPT-5.3-Codex (xhigh) is stronger in agents & tools, coding, instruction following.
- Kimi K3.1 Pro is stronger in composite, knowledge, long context, multimodal, reasoning.
- Kimi K3.1 Pro is 1.2× cheaper ($4.00 vs $4.81 per 1M blended).
At a glance
| Spec | GPT-5.3-Codex | Kimi K3.1 Pro |
|---|---|---|
| Developer | OpenAI | Moonshot AI |
| Released | 5 Feb 2026 | 23 Oct 2026 |
| Context window (tokens) | 400,000 | 1,048,576 |
| Compared at | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro |
| Open weights | No | No |
| Price in / out / cached input per 1M | $1.75 / $14.00 / $0.175 | $2.00 / $10.00 / $0.200 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| Reasoning | 68.6 | 81.3 | Kimi K3.1 Pro |
| Coding | 60.9 | – | not comparable |
| Agents & tools | 80.1 | 67.3 | GPT-5.3-Codex |
| Knowledge | 66.4 | 74.8 | Kimi K3.1 Pro |
| Instruction following | 73.5 | – | not comparable |
| Multimodal | 61.5 | 67.0 | Kimi K3.1 Pro |
| Long context | 66.8 | 68.3 | Kimi K3.1 Pro |
| Composite | 65.8 | 75.8 | Kimi K3.1 Pro |
Where sources disagree
Across the 7 benchmarks both have, GPT-5.3-Codex leads 1, Kimi K3.1 Pro leads 6. Every overall index that rates both puts Kimi K3.1 Pro ahead.
| Overall index | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro | Leader |
|---|---|---|---|
| BenchLeader Index | 63.8 | 66.8 | neck and neck |
| AA Intelligence Index v4.3.2 | 32.5 | 55.1 | Kimi K3.1 Pro |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro |
|---|---|---|
| Blended price per 1M tokens | $4.81 | $4.00 |
| Input / output per 1M | $1.75 / $14.00 | $2.00 / $10.00 |
| Cached input per 1M | $0.175 | $0.200 |
| Price per task: chat reply | $0.0049 | $0.0038 |
| Price per task: summarize a 30-page report | $0.029 | $0.030 |
| Price per task: code edit | $0.032 | $0.027 |
| Price per task: agentic coding session | $0.161 | $0.160 |
| Price per task: structured extraction | $0.0063 | $0.0060 |
| Cost to run a full benchmark suite | – | $2.11 |
| Output speed | 84 tok/s | 81 tok/s |
| Time to first answer | 60.6 s | 27.7 s |
| Time to a complete answer | 66.6 s | 33.9 s |
Each model is compared at its best-ranked reasoning-effort setting. GPT-5.3-Codex (xhigh) scores 63.8 and takes 61 s to its first answer token. Kimi K3.1 Pro scores 66.8 and takes 28 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
45 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 63.8 | 66.8 |
| Agents & tools score | BenchLeader | 80.1 | 67.3 |
| Coding score | BenchLeader | 60.9 | – |
| Composite score | BenchLeader | 65.8 | 75.8 |
| Instruction following score | BenchLeader | 73.5 | – |
| Knowledge score | BenchLeader | 66.4 | 74.8 |
| Long context score | BenchLeader | 66.8 | 68.3 |
| Multimodal score | BenchLeader | 61.5 | 67.0 |
| Reasoning score | BenchLeader | 68.6 | 81.3 |
| Blended price $/M | BenchLeader | $4.81 | $4.00 |
| Output speed | BenchLeader | 84 tok/s | 81 tok/s |
| Time to first answer | BenchLeader | 60.6 s | 27.7 s |
| Context window | BenchLeader | 400,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| LiveBench 2.0 Reasoning | LiveBench | – | 91.1% |
| GPQA Diamond (AA) | Artificial Analysis | 91.5% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.5% | 52.5% |
| CritPt | Artificial Analysis | 16.9% | 24.6% |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 74.8%† | – |
| WeirdML | WeirdML | 77.9% | – |
| Arena WebDev | Arena (LMArena) | 1407† | – |
| LiveBench 2.0 Coding | LiveBench | – | 81.8% |
| SciCode (AA) | Artificial Analysis | – | 60.8% |
| LiveCodeBench | Vals AI | 87.3% | – |
| IOI | Vals AI | 53.8% | – |
| SWE-bench (Vals) | Vals AI | 78.0% | – |
| Vibe Code Bench v1.1 | Vals AI | 61.8% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 1655.2 | – |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 67.2% |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 47.1% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 78.4%† | – |
| LiveBench Long Agentic | LiveBench | – | 49.5% |
| LiveBench Browser | LiveBench | – | 80.0% |
| Terminal-Bench Hard | Artificial Analysis | 53.0% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 86.0% | – |
| HiL-Bench | Scale AI SEAL | 4.3%† | – |
| GDPval-AA v2.1 | Artificial Analysis | – | 67.3% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 64.0%† | – |
| METR Time Horizons | METR | 74.5%† | – |
Show all 45 benchmarks
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Kimi K3.1 Pro |
|---|---|---|---|
| Agents & tools | |||
| Vending-Bench 2 | Andon Labs | 5940.1† | – |
| AutomationBench | Artificial Analysis | – | 73.3% |
| GDP.pdf (AA) | Artificial Analysis | – | 31.4% |
| AA-Briefcase v1.1 | Artificial Analysis | – | 1678 |
| CWE-Bench (AA) | Artificial Analysis | – | 60.8% |
| DeepSec-Bench (AA) | Artificial Analysis | – | 36.7% |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 94.7% |
| Knowledge | |||
| LiveBench 2.0 Data Analysis | LiveBench | – | 79.1% |
| LiveBench Research | LiveBench | – | 77.9% |
| AA-Omniscience | Artificial Analysis | 10.9 | 29.5 |
| AA-Omniscience: accuracy | Artificial Analysis | 52.9% | 51.0% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 10.8% | 56.1% |
| Instruction following | |||
| IFBench | Artificial Analysis | 75.4% | – |
| Multimodal | |||
| LiveBench Design | LiveBench | – | 77.8% |
| MMMU-Pro | Artificial Analysis | 78.5% | 83.9% |
| Long context | |||
| AA-LCR | Artificial Analysis | 83.3% | 86.3% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 157.1† | – |
| LiveBench 2.0 | LiveBench | – | 76.7% |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 32.5 | 55.1 |
| AA Cyber Index | Artificial Analysis | – | 64.1 |
Data as of 2026-10-24. Best configuration of each model; every score links to its source on the model pages.
What changed
- Kimi K3.1 Pro: BenchLeader Index 65.6 → 66.8.
- Kimi K3.1 Pro: 5 new results (FrontierCode 1.1 (AA), AA Cyber Index, CWE-Bench (AA)…).
- Kimi K3.1 Pro: first results.
- GPT-5.3-Codex (xhigh): BenchLeader Index 64.4 → 63.9.
- GPT-5.3-Codex (xhigh): BenchLeader Index 64.9 → 64.4.
Related comparisons
- GPT-5.3-Codex vs Claude Opus 5.5
- GPT-5.3-Codex vs Gemini 4 Argon
- GPT-5.3-Codex vs Muse Wonder Full
- Kimi K3.1 Pro vs Claude Opus 5.5
- Kimi K3.1 Pro vs Gemini 4 Argon
- Kimi K3.1 Pro vs Muse Wonder Full
- Kimi K3.1 Pro vs Claude Fable 5.1
- GPT-5.3-Codex vs Kimi K3
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GPT-5.3-Codex vs Kimi K3.1 Pro: questions
- Is GPT-5.3-Codex better than Kimi K3.1 Pro?
- GPT-5.3-Codex (xhigh) and Kimi K3.1 Pro are neck and neck on quality (63.8 vs 66.8, within the ±7.9 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-24, so check the category scores for your use.
- Which is cheaper, GPT-5.3-Codex or Kimi K3.1 Pro, and will that change?
- Kimi K3.1 Pro is cheaper today: $4.00 against $4.81 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is GPT-5.3-Codex better than Kimi K3.1 Pro for agents and tool use?
- GPT-5.3-Codex scores higher in agentic tasks (80 vs 67 on the category index, where 50 is average).
- Which is better for long documents?
- Kimi K3.1 Pro scores higher on long-context benchmarks (68 vs 67). Kimi K3.1 Pro accepts more context: 1,048,576 against 400,000 tokens for GPT-5.3-Codex.
- Which has lower latency?
- Kimi K3.1 Pro answers first: 27.7 s against 60.6 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Which is faster, GPT-5.3-Codex or Kimi K3.1 Pro?
- GPT-5.3-Codex streams faster: 84 against 81 output tokens per second.
- Do independent benchmarks agree on GPT-5.3-Codex vs Kimi K3.1 Pro?
- Across the 7 benchmarks both have, GPT-5.3-Codex leads 1, Kimi K3.1 Pro leads 6. Every overall index that rates both puts Kimi K3.1 Pro ahead.
- When was Kimi K3.1 Pro released?
- Kimi K3.1 Pro was released on 23 Oct 2026.