Model comparison
GPT-5.2 Codex vs Llama 4 Maverick
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 30.9 on the Noometry Index. Llama 4 Maverick costs 16× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. GPT-5.2 Codex scores higher in 2 categories and Llama 4 Maverick in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where GPT-5.2 Codex leads 45.5 to 26.6.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 72.8% for GPT-5.2 Codex and 21% for Llama 4 Maverick.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 128K.
- Llama 4 Maverick has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 Codex | Llama 4 Maverick | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 42.6 | 30.9 |
| Released | 2025-12-18 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $1.75 | $0.19 |
| Output $ / M tokens | $14 | $0.65 |
| Results tracked | 5 | 54 |
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Category by category
Coding GPT-5.2 Codex leads
GPT-5.2 Codex: 45.5 (#71), Llama 4 Maverick: 26.6 (#324)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | 21% |
| ALE-Bench | 1,300 | 172.97 |
| Aider Polyglot | — | 15.6% |
| LMArena WebDev | 1339 | — |
| SWE-bench Multilingual | 66.3% | — |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| BigCodeBench Instruct | — | 49.7% |
| LMArena Coding | — | 1302 |
| BigCodeBench Complete | — | 61.4% |
Agentic & Tool Use GPT-5.2 Codex leads
GPT-5.2 Codex: 41.0 (#22), Llama 4 Maverick: 28.2 (#91)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| Terminal-Bench | 66.5% | — |
| Berkeley Function Calling Leaderboard | — | 37.3% |
Reasoning Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 10.1 (#342)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| EnigmaEval | — | 0.6% |
| LMArena Hard Prompts | — | 1281 |
| DTBench | — | 61.9% |
| LMCA | — | 15.9% |
| Epoch Capabilities Index | — | 132.2 |
| ForecastBench | — | 57.5 |
Math Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 26.0 (#262)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 20.6% |
| Omni-MATH | — | 42.2% |
| LMArena Math | — | 1299 |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 33.4 (#204)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | — | 67% |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |
| LMArena Expert | — | 1259 |
Multimodal Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 31.6 (#105)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | — | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 42.2 (#195)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | — | 1269 |
| LMArena Chinese | — | 1277 |
| LMArena French | — | 1259 |
| LMArena German | — | 1291 |
| LMArena Japanese | — | 1207 |
| LMArena Korean | — | 1203 |
| LMArena Russian | — | 1286 |
| LMArena Spanish | — | 1293 |
Instruction Following Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 71.7 (#146)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| IFEval | — | 90.8% |
| LMArena Instruction Following | — | 1267 |
Long Context Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 31.4 (#279)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| Fiction.LiveBench | — | 46.2% |
| LMArena Longer Query | — | 1280 |
Writing & Preference Not comparable
GPT-5.2 Codex: —, Llama 4 Maverick: 38.8 (#252)
| Benchmark | GPT-5.2 Codex | Llama 4 Maverick |
|---|---|---|
| LMArena Text | — | 1287 |
| LMArena Creative Writing | — | 1267 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |
| LMArena Multi-Turn | — | 1289 |
Frequently asked questions
Is GPT-5.2 Codex better than Llama 4 Maverick?
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 30.9 on the Noometry Index. Llama 4 Maverick costs 16× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 Codex or Llama 4 Maverick?
Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.
Is GPT-5.2 Codex or Llama 4 Maverick better for coding?
GPT-5.2 Codex scores higher on coding benchmarks: 45.5 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 Codex does, with 400K tokens against 128K.
How many benchmarks do GPT-5.2 Codex and Llama 4 Maverick share?
2 benchmarks have published results for both models. GPT-5.2 Codex has 5 scored results on Noometry and Llama 4 Maverick has 54.