Llama 3.1 405B vs GPT-5.6 Sol
At a Glance
| Compare | Llama 3.1 405BMeta | GPT-5.6 SolOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #5 of 4685.3 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #24 of 44$0.117 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #3 of 3865.3 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.00Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $10.00Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 1,050K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 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
Side-by-Side Facts
| Field | Llama-3.1-405B | GPT-5.6 Sol |
|---|---|---|
| Developer | Meta | OpenAI |
| Family | Llama 3 1 405b | Gpt 5 6 |
| Model | Llama-3.1-405B | GPT-5.6 Sol |
| Version | Llama-3.1-405B | GPT-5.6 Sol |
| Lifecycle | active | active |
| Released | 2024-07-23 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 1,050K |
| Total parameters | 405.9B | Unknown |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
Llama 3.1 405B Capabilities
GPT-5.6 Sol Capabilities
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 405B vs GPT-5.6 Sol FAQs
Is Llama 3.1 405B or GPT-5.6 Sol better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B and GPT-5.6 Sol, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B or GPT-5.6 Sol?+
Only GPT-5.6 Sol has a directly sourced input price: $2.00 per million tokens. Only GPT-5.6 Sol has a directly sourced output price: $10.00 per million tokens.
Which has a larger context window, Llama 3.1 405B or GPT-5.6 Sol?+
GPT-5.6 Sol has the larger sourced context window. Llama 3.1 405B supports 131K and GPT-5.6 Sol supports 1,050K.
Which performs better in benchmarks, Llama 3.1 405B or GPT-5.6 Sol?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 405B or GPT-5.6 Sol be self-hosted?+
Llama 3.1 405B is the only model in this pair currently marked as self-hostable. Llama 3.1 405B is open weight; GPT-5.6 Sol is not marked open weight.
Can Llama 3.1 405B and GPT-5.6 Sol understand images?+
Llama 3.1 405B is not documented with image input; GPT-5.6 Sol is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 405B or GPT-5.6 Sol?+
Neither has a larger sourced maximum output. Llama 3.1 405B is — and GPT-5.6 Sol is 128K.
Do Llama 3.1 405B and GPT-5.6 Sol support reasoning and tool use?+
Llama 3.1 405B: none of these features are definitively sourced. GPT-5.6 Sol: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 405B or GPT-5.6 Sol?+
Llama 3.1 405B has 1 sourced provider route; GPT-5.6 Sol has 2, so GPT-5.6 Sol has broader tracked availability.
Which offers better value, Llama 3.1 405B or GPT-5.6 Sol?+
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.