Llama-4-Scout-17B-16E-Instruct vs GPT-5.6 Luna
Benchmark Performance
Available Benchmarks
| Benchmark | Llama-4-Scout-17B-16E-Instruct | GPT-5.6 Luna |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,279.2589% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.53478136, 1283.95578792]; votes 29739; rank 255 | 1,430.14100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.14784950, 1435.13857729]; votes 24433; rank 82 |
| LMArena Vision Arenavision-2026-08-27-011508720696 · arena_rating · leader | 1,118.0889% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.62969338, 1127.52426769]; votes 6467; rank 110 | 1,254.30100% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1245.05722338, 1263.53552757]; votes 5857; rank 45 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 2 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.
Technical Differences
Side-by-Side Facts
| Field | Llama-4-Scout-17B-16E-Instruct | GPT-5.6 Luna |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Llama 4 Scout 17b 16e Instruct | Gpt 5 6 |
| Model | Llama-4-Scout-17B-16E-Instruct | GPT-5.6 Luna |
| Version | Llama-4-Scout-17B-16E-Instruct | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2025-04-05 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 10,000,000 | 1,050,000 |
| Total parameters | 108,641,793,536 | Unknown |
| Active parameters | 17,000,000,000 | 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, tools |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Llama-4-Scout-17B-16E-Instruct Capabilities
GPT-5.6 Luna Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Llama-4-Scout-17B-16E-Instruct vs GPT-5.6 Luna FAQs
Is Llama-4-Scout-17B-16E-Instruct or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama-4-Scout-17B-16E-Instruct and GPT-5.6 Luna, 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-5.6 Luna?+
Llama-4-Scout-17B-16E-Instruct is $0.10 and GPT-5.6 Luna is $0.20 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-5.6 Luna is $1.20 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-5.6 Luna?+
Llama-4-Scout-17B-16E-Instruct has the larger sourced context window. Llama-4-Scout-17B-16E-Instruct supports 10,000,000 and GPT-5.6 Luna supports 1,050,000.
Which performs better in benchmarks, Llama-4-Scout-17B-16E-Instruct or GPT-5.6 Luna?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Llama-4-Scout-17B-16E-Instruct or GPT-5.6 Luna 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-5.6 Luna is not marked open weight.
Can Llama-4-Scout-17B-16E-Instruct and GPT-5.6 Luna understand images?+
Llama-4-Scout-17B-16E-Instruct is documented with image input; GPT-5.6 Luna 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-5.6 Luna?+
Neither has a larger sourced maximum output. Llama-4-Scout-17B-16E-Instruct is — and GPT-5.6 Luna is 128,000.
Do Llama-4-Scout-17B-16E-Instruct and GPT-5.6 Luna support reasoning and tool use?+
Llama-4-Scout-17B-16E-Instruct: tool calling and image input. GPT-5.6 Luna: 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-5.6 Luna?+
Llama-4-Scout-17B-16E-Instruct has 4 sourced provider routes; GPT-5.6 Luna 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-5.6 Luna?+
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.