Llama 4 Scout 17B 16E Instruct vs GPT-5.2
At a Glance
| Compare |  GPT-5.2OpenAI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #33 of 4649.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #28 of 44$0.161 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #34 of 3844.0 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $1.75Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | $14.00Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 10,000K | 400K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 3, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Llama-4-Scout-17B-16E-Instruct | GPT-5.2 |
|---|---|---|
| Berkeley Function Calling Leaderboardv4-ede5081a24bc · overall_accuracy · leader | 28.1362% of row best · percent · Llama-4-Scout-17B-16E-Instruct (FC) | 45.27100% of row best · percent · GPT-5.2-2025-12-11 (Prompt) |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,279.2991% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259 | 1,412.43100% of row best · rating · gpt-5.2; 95% CI [1409.21136225, 1415.65317114]; votes 78967; rank 125 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,117.8091% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 | 1,229.44100% of row best · rating · gpt-5.2; 95% CI [1223.25930119, 1235.62760198]; votes 18997; rank 68 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 47.3261% of row best · points · Llama 4 Scout · 696 output tokens / case | 78.08100% of row best · points · GPT-5.2 · 4,266 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 3 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Llama-4-Scout-17B-16E-Instruct | GPT-5.2 |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Llama 4 Scout 17b 16e Instruct | Gpt 5 2 |
| Model | Llama-4-Scout-17B-16E-Instruct | GPT-5.2 |
| Version | Llama-4-Scout-17B-16E-Instruct | GPT-5.2 |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2025-12-11 |
| Knowledge cutoff | Unknown | 2025-08-31 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 10,000K | 400K |
| Total parameters | 108.6B | Unknown |
| Active parameters | 17B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, structured_outputs, tools |
Llama 4 Scout 17B 16E Instruct Capabilities
GPT-5.2 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs GPT-5.2 FAQs
Is Llama 4 Scout 17B 16E Instruct or GPT-5.2 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and GPT-5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
Llama 4 Scout 17B 16E Instruct is $0.10 and GPT-5.2 is $1.75 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Llama 4 Scout 17B 16E Instruct is $0.30 and GPT-5.2 is $14.00 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.
Which has a larger context window, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and GPT-5.2 supports 400K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
GPT-5.2 leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Llama 4 Scout 17B 16E Instruct or GPT-5.2 be self-hosted?+
Llama 4 Scout 17B 16E Instruct is the only model in this pair currently marked as self-hostable. Llama 4 Scout 17B 16E Instruct is open weight; GPT-5.2 is not marked open weight.
Can Llama 4 Scout 17B 16E Instruct and GPT-5.2 understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; GPT-5.2 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and GPT-5.2 is 128K.
Do Llama 4 Scout 17B 16E Instruct and GPT-5.2 support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. GPT-5.2: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; GPT-5.2 has 2, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.
Which offers better value, Llama 4 Scout 17B 16E Instruct or GPT-5.2?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.