Model comparison
GPT-5.2-Codex vs Llama 4 Scout
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.2-Codex #58 (Supported); Llama 4 Scout #174 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2-Codex and Llama 4 Scout share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to GPT-5.2-Codex; 6 to Llama 4 Scout.
Updated July 20, 2026- Shared results
- 12
- GPT-5.2-Codex only
- 3
- Llama 4 Scout only
- 6
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.2-Codex and Llama 4 Scout is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-5.2-Codex is priced at $1.75 input / $14.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 4 Scout. Llama 4 Scout has the larger context window at 10M, compared with 400K for GPT-5.2-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.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.2-Codex | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2-Codex$1.75 input / $14 output | Llama 4 Scout$0 input / $0 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.2-Codex123 tok/s | Llama 4 Scout128 tok/s | Llama 4 Scout has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.2-Codex87.34 s | Llama 4 Scout0.70 s | Llama 4 Scout reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.2-Codex400K | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic6 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5.2-Codex | Llama 4 Scout | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 40.1% | 10.0% | GPT-5.2-Codex leads |
| AA-GPQA DiamondSource | 89.9% | 58.7% | GPT-5.2-Codex leads |
| AA-HLESource | 33.5% | 4.3% | GPT-5.2-Codex leads |
| AA-Omniscience IndexSource | -2.5% | -52.4% | GPT-5.2-Codex leads |
| AA-Omniscience AccuracySource | 40.7% | 14.6% | GPT-5.2-Codex leads |
| AA-Omniscience Hallucination RateSource | 72.8% | 78.3% | GPT-5.2-Codex leads |
Math1 benchmarks
| Benchmark | GPT-5.2-Codex | Llama 4 Scout | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | — | 0.000% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.2-Codex | Llama 4 Scout | Result |
|---|---|---|---|
| AA-IFBenchSource | 77.6% | 39.5% | GPT-5.2-Codex leads |
Frequently Asked Questions (3)
Can I compare GPT-5.2-Codex and Llama 4 Scout on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-5.2-Codex and Llama 4 Scout today?
GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens Llama 4 Scout: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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