Llama 3.1 405B Instruct vs Sonar Reasoning Pro
At a Glance
| Compare |  Sonar Reasoning ProPerplexity | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $2.00Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $8.00Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 128K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 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-Instruct | Sonar Reasoning Pro |
|---|---|---|
| Developer | Meta | Perplexity |
| Family | Llama 3 1 405b Instruct | Sonar |
| Model | Llama-3.1-405B-Instruct | Sonar Reasoning Pro |
| Version | Llama-3.1-405B-Instruct | Sonar Reasoning Pro |
| Lifecycle | active | active |
| Released | 2024-07-23 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 128K |
| 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) | Openrouter (Standard), Perplexity (Standard) |
| Capabilities | chat, generation, tools | chat, citations, reasoning, search |
Llama 3.1 405B Instruct Capabilities
Sonar Reasoning Pro Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 3.1 405B Instruct vs Sonar Reasoning Pro FAQs
Is Llama 3.1 405B Instruct or Sonar Reasoning Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B Instruct and Sonar Reasoning Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
Only Sonar Reasoning Pro has a directly sourced input price: $2.00 per million tokens. Only Sonar Reasoning Pro has a directly sourced output price: $8.00 per million tokens.
Which has a larger context window, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
Llama 3.1 405B Instruct has the larger sourced context window. Llama 3.1 405B Instruct supports 131K and Sonar Reasoning Pro supports 128K.
Which performs better in benchmarks, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
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 Instruct or Sonar Reasoning Pro be self-hosted?+
Llama 3.1 405B Instruct is the only model in this pair currently marked as self-hostable. Llama 3.1 405B Instruct is open weight; Sonar Reasoning Pro is not marked open weight.
Can Llama 3.1 405B Instruct and Sonar Reasoning Pro understand images?+
Llama 3.1 405B Instruct is not documented with image input; Sonar Reasoning Pro is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
Neither has a larger sourced maximum output. Llama 3.1 405B Instruct is — and Sonar Reasoning Pro is —.
Do Llama 3.1 405B Instruct and Sonar Reasoning Pro support reasoning and tool use?+
Llama 3.1 405B Instruct: tool calling. Sonar Reasoning Pro: reasoning. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
Llama 3.1 405B Instruct has 1 sourced provider route; Sonar Reasoning Pro has 2, so Sonar Reasoning Pro has broader tracked availability.
Which offers better value, Llama 3.1 405B Instruct or Sonar Reasoning Pro?+
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.