GPT-5.3-Codex vs Grok 4.7
Full profiles: GPT-5.3-Codex (OpenAI) and Grok 4.7 (SpaceXAI): every benchmark, price and rank.
Updated · Data from 12 sources
As of 6 Nov 2026, GPT-5.3-Codex and Grok 4.7 are neck and neck on quality (62.9 vs 58.3 on the BenchLeader Index, inside the ±7.4 margin), and Grok 4.7 is 1.6× cheaper at $3.00 vs $4.81 per 1M tokens blended. GPT-5.3-Codex leads in every category we measure both in.
Key facts (as of 6 Nov 2026)
- GPT-5.3-Codex scores 62.9 on the BenchLeader Index and Grok 4.7 scores 58.3 (BenchLeader, 6 Nov 2026).
- GPT-5.3-Codex costs $4.81 per 1M tokens blended (list price, 6 Nov 2026).
- Grok 4.7 costs $3.00 per 1M tokens blended (list price, 6 Nov 2026).
- Of the 7 benchmarks both models have, Grok 4.7 leads 6 (BenchLeader, 6 Nov 2026).
GPT-5.3-Codex benchmarks 
Grok 4.7 benchmarks Which 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 |
|---|---|---|---|
| Coding | Either | 61 vs 62 category score; IOI 53.8% vs 57.7% | Either |
| Agents & tool use | GPT-5.3-Codex | 80 vs 57 category score; AA-Briefcase v1.1 868 vs 1637 | High |
| Long documents | GPT-5.3-Codex | 67 vs 63 category score; AA-LCR v1.1 83.3% vs 76.7% | Medium |
| Images and other media | GPT-5.3-Codex | 61 vs 56 category score | Medium |
| Chat quality (human preference) | – | Only Grok 4.7 has an Arena rating (62 category score) | Not comparable |
| Lowest cost | Grok 4.7 | $3.00 vs $4.81 per 1M blended; agentic coding session $0.144 vs $0.161 | – |
| Fastest first answer | Either | 55.5 s (GPT-5.3-Codex (xhigh)) vs 55.1 s (Grok 4.7 (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 Grok 4.7 (xhigh) are neck and neck on quality (62.9 vs 58.3, within the ±7.4 margin).
- GPT-5.3-Codex (xhigh) is stronger in agents & tools, instruction following, long context, multimodal.
- Grok 4.7 (xhigh) is stronger in coding, human preference, knowledge, maths, reasoning.
- Grok 4.7 (xhigh) is 1.6× cheaper ($3.00 vs $4.81 per 1M blended).
At a glance
| Spec | GPT-5.3-Codex | Grok 4.7 |
|---|---|---|
| Developer | OpenAI | SpaceXAI |
| Released | 5 Feb 2026 | 21 Sep 2026 |
| Context window (tokens) | 400,000 | 500,000 |
| Compared at | GPT-5.3-Codex (xhigh) | Grok 4.7 (xhigh) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $1.75 / $14.00 / $0.175 | $2.00 / $6.00 / $0.500 |
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) | Grok 4.7 (xhigh) | Leader |
|---|---|---|---|
| Reasoning | – | 56.4 | not comparable |
| Coding | 60.9 | 61.5 | level |
| Agents & tools | 80.0 | 56.9 | GPT-5.3-Codex |
| Maths | – | 55.9 | not comparable |
| Knowledge | – | 61.1 | not comparable |
| Instruction following | 73.5 | – | not comparable |
| Human preference | – | 62.3 | not comparable |
| Multimodal | 60.9 | 55.7 | GPT-5.3-Codex |
| Long context | 66.5 | 63.1 | GPT-5.3-Codex |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Reasoning, Maths, Knowledge, Instruction following, Human preference.
ModelIndex
Where sources disagree
Across the 7 benchmarks both have, GPT-5.3-Codex leads 1, Grok 4.7 leads 6.
| Overall index | GPT-5.3-Codex (xhigh) | Grok 4.7 (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 62.9 | 58.3 | neck and neck |
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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | GPT-5.3-Codex (xhigh) | Grok 4.7 (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $4.81 | $3.00 |
| Input / output per 1M | $1.75 / $14.00 | $2.00 / $6.00 |
| Cached input per 1M | $0.175 | $0.500 |
| Price per task: chat reply | $0.0049 | $0.0026 |
| Price per task: summarize a 30-page report | $0.029 | $0.028 |
| Price per task: code edit | $0.032 | $0.021 |
| Price per task: agentic coding session | $0.161 | $0.144 |
| Price per task: structured extraction | $0.0063 | $0.0052 |
| Agentic coding session, input mostly cached | $0.090 | $0.076 |
| Cost per benchmark task (AA Index) | – | $7.77 |
| Output tokens per task (AA Index) | – | 105k |
| Of which reasoning | – | 69k |
| Time per task (AA Index) | – | 24 min |
| Output speed | 74 tok/s | 71 tok/s |
| Time to first answer | 55.5 s | 55.1 s |
| Time to a complete answer | 111.8 s | 147.2 s |
Each model is compared at its best-ranked reasoning-effort setting. GPT-5.3-Codex (xhigh) scores 62.9 and takes 55 s to its first answer token. Grok 4.7 (xhigh) scores 58.3 and takes 55 s to its first answer token; Grok 4.7 (high) scores 57.7 in 31 s, Grok 4.7 (low) scores 56.0 in 9 s. 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
131 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) | Grok 4.7 (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 62.9 | 58.3 |
| Agents & tools score | BenchLeader | 80.0 | 56.9 |
| Coding score | BenchLeader | 60.9 | 61.5 |
| Instruction following score | BenchLeader | 73.5 | – |
| Long context score | BenchLeader | 66.5 | 63.1 |
| Multimodal score | BenchLeader | 60.9 | 55.7 |
| Human preference score | BenchLeader | – | 62.3 |
| Knowledge score | BenchLeader | – | 61.1 |
| Maths score | BenchLeader | – | 55.9 |
| Reasoning score | BenchLeader | – | 56.4 |
| Blended price $/M | BenchLeader | $4.81 | $3.00 |
| Output speed | BenchLeader | 74 tok/s | 71 tok/s |
| Time to first answer | BenchLeader | 55.5 s | 55.1 s |
| Context window | BenchLeader | 400,000 tokens | 500,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 92.7% |
| Arena Hard Prompts | Arena (LMArena) | – | 1463 |
| LiveBench 2.0 Reasoning | LiveBench | – | 82.7%† |
| LiveBench 1.0 Reasoning | LiveBench | – | 82.7% |
| GPQA Diamond (AA) | Artificial Analysis | 91.5% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 42.5% | 43.1% |
| HLE-Diamond (AA) | Artificial Analysis | – | 23.2% |
| ARC-AGI-1 | ARC Prize | – | 89.5% |
| ARC-AGI-2 | ARC Prize | – | 61.4% |
| ARC-AGI-3 | ARC Prize | – | 7.0% |
| CritPt | Artificial Analysis | 16.9% | 17.7% |
| CritPt v2 hard subset (AA) | Artificial Analysis | – | 33.5% |
| MysteryMechanism | Vals AI | – | 25.2% |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 41.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 29.0% |
| LMCA | Epoch AI Benchmarking Hub | – | 49.4% |
| DTBench | Epoch AI Benchmarking Hub | – | 96.0% |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | 74.8%† | – |
| SciCode | SciCode | – | 57.4% |
| WeirdML | WeirdML | 77.9% | – |
| FrontierCode | Cognition | – | 47.6%† |
| Arena Coding | Arena (LMArena) | – | 1494 |
| Arena WebDev | Arena (LMArena) | 1407† | 1638 |
| LiveBench 2.0 Coding | LiveBench | – | 84.5%† |
| LiveBench 1.0 Coding | LiveBench | – | 77.2% |
Show all 131 benchmarks
| Metric | Publisher | GPT-5.3-Codex (xhigh) | Grok 4.7 (xhigh) |
|---|---|---|---|
| Coding | |||
| LiveCodeBench | Vals AI | 87.3% | – |
| IOI | Vals AI | 53.8% | 57.7% |
| SWE-bench (Vals) | Vals AI | 78.0% | – |
| Code Migration | Vals AI | – | 44.8% |
| Code Migration v1.1 | Vals AI | – | 53.2% |
| ProgramBench | Vals AI | – | 0.5% |
| SWE-bench Multimodal | Vals AI | – | 42.9% |
| Vibe Code Bench v1.1 | Vals AI | 61.8% | 86.2% |
| CursorBench | Cursor | – | 46.3% |
| ALE-Bench | Epoch AI Benchmarking Hub | 1655.2 | – |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 29.5% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | – | 25.8% |
| Startrise Frontend | Startrise | – | 75.9%† |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 63.4† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 71.5 |
| FrontierCode 1.1 (AA) | Artificial Analysis | – | 33.8% |
| FrontierCode 2.0 (AA) | Artificial Analysis | – | 28.7% |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 78.4%† | 37.6% |
| APEX-Agents | Mercor | – | 54.6%† |
| Arena Agent | Arena (LMArena) | – | 2.7 |
| LiveBench Long Agentic | LiveBench | – | 46.1%† |
| LiveBench Browser | LiveBench | – | 49.3%† |
| LiveBench Agentic Coding | LiveBench | – | 54.0% |
| Terminal-Bench Hard | Artificial Analysis | 53.0% | – |
| τ²-Bench Telecom (AA) | Artificial Analysis | 86.0% | – |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 73.4% |
| HiL-Bench | Scale AI SEAL | 4.3%† | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 60.7% |
| GDPval-AA v2.1 | Artificial Analysis | – | 1718 |
| ITBench SRE (AA) | Artificial Analysis | – | 42.1% |
| Analyst Agent (AA) | Artificial Analysis | – | 52.5% |
| CyberBench | Vals AI | – | 69.5% |
| Finance Agent v2 | Vals AI | – | 52.3% |
| Finance Agent v2.1 | Vals AI | – | 60.6% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 18.3% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 12.5% |
| Legal Research Bench | Vals AI | – | 47.1% |
| Robo-bench | Vals AI | – | 0.2% |
| SREBench | Vals AI | – | 3.8% |
| Tax Agent Bench | Vals AI | – | 34.2% |
| Terminal-Bench 2.0 (Vals) | Vals AI | 64.0%† | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | – | 28.8% |
| Terminal-Bench Science | Vals AI | – | 10.0% |
| METR Time Horizons | METR | 74.5%† | – |
| Vending-Bench 2 | Andon Labs | 5940.1† | 10536.8† |
| AutomationBench | Artificial Analysis | – | 65.6% |
| GDP.pdf (AA) | Artificial Analysis | – | 20.0% |
| Harvey LAB-AA v1.1 | Harvey | – | 9.4% |
| AA-Briefcase v1.1 | Artificial Analysis | 868 | 1637 |
| GDPx | GDPx by SemiAnalysis | – | 3.9% |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | – | 11.4% |
| CWE-Bench (AA) | Artificial Analysis | – | 68.3% |
| DeepSec-Bench (AA) | Artificial Analysis | – | 26.9% |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 74.0% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 53.0% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 17.1% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 98.1% |
| ProofBench | Vals AI | – | 26.0%† |
| LiveBench Mathematics | LiveBench | – | 95.7% |
| Arena Maths | Arena (LMArena) | – | 1443 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 56.0% |
| LiveBench 2.0 Data Analysis | LiveBench | – | 76.9%† |
| LiveBench Research | LiveBench | – | 86.4%† |
| LiveBench 1.0 Data Analysis | LiveBench | – | 76.9% |
| AA-Omniscience v1.1 | Artificial Analysis | – | 31.2 |
| LegalBench | Vals AI | – | 84.4% |
| PRBench Legal | Scale AI SEAL | – | 47.6% |
| BioMysteryBench | Vals AI | – | 69.3% |
| Excel Modeling Benchmark | Vals AI | – | 67.0% |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 71.5% |
| MedCode | Vals AI | – | 49.5% |
| MedScribe | Vals AI | – | 89.4% |
| Public Benefits Bench v1.1 | Vals AI | – | 65.6% |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 22.8% |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | – | 42.7% |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | – | 80.0% |
| HealthBench Professional (AA) | OpenAI | – | 55.8% |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 80.1% |
| LiveBench Instruction Following | LiveBench | – | 75.3% |
| IFBench | Artificial Analysis | 75.4% | – |
| Arena Instruction Following | Arena (LMArena) | – | 1438 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1441 |
| Arena Creative Writing | Arena (LMArena) | – | 1419 |
| Arena Multi-turn | Arena (LMArena) | – | 1432 |
| Arena Longer Queries | Arena (LMArena) | – | 1452 |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1231 |
| LiveBench Design | LiveBench | – | 78.0%† |
| MMMU-Pro | Artificial Analysis | 78.5% | 78.2%† |
| SAGE | Vals AI | – | 88.7% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 32.5%† |
| Code2Video-AA | Artificial Analysis | – | 1127 |
| AA-3D | Artificial Analysis | – | 995 |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 76.7% |
| MLCR | Artificial Analysis | – | 20.0% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 156.1† |
| LiveBench 2.0 | LiveBench | – | 70.1%† |
| LiveBench 1.0 | LiveBench | – | 77.4% |
| AA Intelligence Index v5.0 | Artificial Analysis | – | 35.5 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 32.5 | 46.5 |
| AA Progress Index v1 | Artificial Analysis | 301.3 | 322.7 |
| Vals Index | Vals AI | – | 58.6 |
| Vals Finance Index | Vals AI | – | 58.5% |
| Vals RSI Index | Vals AI | – | 20.2% |
| Vals Science Index | Vals AI | – | 34.8% |
| Vals Router Index | Vals AI | – | 58.6%† |
| AA Cyber Index | Artificial Analysis | – | 56.4 |
Data as of 2026-11-06. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-09b2218cbd85 (https://benchmarks.duellab.org/releases/gb2-20261103-09b2218cbd85/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- Grok 4.7 (xhigh): 2 score revisions.
- Grok 4.7 (xhigh): 2 score revisions.
- GPT-5.3-Codex (xhigh): 1 new result (AA-Briefcase v1.1).
- Grok 4.7 (xhigh): 1 score revision.
- Grok 4.7 (xhigh): BenchLeader Index 59.5 → 58.0.
- GPT-5.3-Codex (xhigh): 1 new result (AA Progress Index v1).
- Grok 4.7 (xhigh): 5 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 4 score revisions; cost per task moved from $3.24/M to $7.51/M (132% up).
- Grok 4.7 (xhigh): cost per task moved from $3.74/M to $3.24/M (13% down).
- Grok 4.7 (xhigh): 9 new results (Code Migration v1.1, Excel Modeling Benchmark v1.1, Finance Agent v2.1…).
- Grok 4.7 (xhigh): 2 score revisions.
Related comparisons
- GPT-5.3-Codex vs Claude Opus 5.5
- GPT-5.3-Codex vs Gemini 4 Carbon
- GPT-5.3-Codex vs GPT-6 Astra
- Grok 4.7 vs Gemini 4 Argon
- Grok 4.7 vs GPT-6.1 Terra
- Grok 4.7 vs Claude Opus 4.7
- GPT-5.3-Codex vs Claude Fable 5.1
- GPT-5.3-Codex vs Claude Fable 5
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
GPT-5.3-Codex vs Grok 4.7: questions
- Is GPT-5.3-Codex better than Grok 4.7?
- GPT-5.3-Codex (xhigh) and Grok 4.7 (xhigh) are neck and neck on quality (62.9 vs 58.3, within the ±7.4 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 6 Nov 2026, so check the category scores for your use.
- GPT-5.3-Codex vs Grok 4.7: which is cheaper, and will that change?
- Grok 4.7 is cheaper today: $3.00 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 or Grok 4.7 better for coding?
- They are level in coding (61 vs 62 on the category index). On IOI, the top shared coding benchmark, it is 53.8% for GPT-5.3-Codex against 57.7% for Grok 4.7.
- Which is faster, GPT-5.3-Codex or Grok 4.7?
- GPT-5.3-Codex streams faster (74 vs 71 output tokens per second, Artificial Analysis 7-day median).
- Is GPT-5.3-Codex or Grok 4.7 better for agents and tool use?
- GPT-5.3-Codex scores higher in agentic tasks (80 vs 57 on the category index, where 50 is average).
More questions (4)
- Which is better for long documents?
- GPT-5.3-Codex scores higher on long-context benchmarks (67 vs 63). Grok 4.7 accepts more context: 500,000 against 400,000 tokens for GPT-5.3-Codex.
- Which has lower latency?
- Grok 4.7 answers first: 55.1 s against 55.5 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.
- Do independent benchmarks agree on GPT-5.3-Codex vs Grok 4.7?
- Across the 7 benchmarks both have, GPT-5.3-Codex leads 1, Grok 4.7 leads 6.
- When was Grok 4.7 released?
- Grok 4.7 was released on 21 Sep 2026.