Llama 3.1 8B vs Llama 4 Maverick 17B 128E Instruct
At a Glance
| Compare |  Llama 3.1 8BMeta | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.1875Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.6525Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 1,000K |
| 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-3.1-8B | Llama-4-Maverick-17B-128E-Instruct |
|---|---|---|
| Developer | Meta | Meta |
| Family | Llama 3 1 8b | Llama 4 Maverick 17b 128e Instruct |
| Model | Llama-3.1-8B | Llama-4-Maverick-17B-128E-Instruct |
| Version | Llama-3.1-8B | Llama-4-Maverick-17B-128E-Instruct |
| Lifecycle | active | active |
| Released | 2024-07-23 | 2025-04-05 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | 8B | 401.6B |
| Active parameters | Unknown | 17B |
| License | llama3.1 | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Openrouter (Standard) |
| Capabilities | generation | chat, generation, tools |
Llama 3.1 8B Capabilities
Llama 4 Maverick 17B 128E Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 3.1 8B vs Llama 4 Maverick 17B 128E Instruct FAQs
Is Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B and Llama 4 Maverick 17B 128E Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
Only Llama 4 Maverick 17B 128E Instruct has a directly sourced input price: $0.1875 per million tokens. Only Llama 4 Maverick 17B 128E Instruct has a directly sourced output price: $0.6525 per million tokens.
Which has a larger context window, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
Llama 4 Maverick 17B 128E Instruct has the larger sourced context window. Llama 3.1 8B supports 131K and Llama 4 Maverick 17B 128E Instruct supports 1,000K.
Which performs better in benchmarks, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 8B is open weight; Llama 4 Maverick 17B 128E Instruct is open weight.
Can Llama 3.1 8B and Llama 4 Maverick 17B 128E Instruct understand images?+
Llama 3.1 8B is not documented with image input; Llama 4 Maverick 17B 128E Instruct is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
Neither has a larger sourced maximum output. Llama 3.1 8B is — and Llama 4 Maverick 17B 128E Instruct is —.
Do Llama 3.1 8B and Llama 4 Maverick 17B 128E Instruct support reasoning and tool use?+
Llama 3.1 8B: none of these features are definitively sourced. Llama 4 Maverick 17B 128E Instruct: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
Llama 3.1 8B has 1 sourced provider route; Llama 4 Maverick 17B 128E Instruct has 1, a tie.
Which offers better value, Llama 3.1 8B or Llama 4 Maverick 17B 128E Instruct?+
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.