MolmoAct2 Pretrain vs Llama 3.1 8B Instruct
At a Glance
| Compare | ||
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.050Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.080Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 16K | 131K |
| 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 | MolmoAct2-Pretrain | Llama-3.1-8B-Instruct |
|---|---|---|
| Developer | Ai2 | Meta |
| Family | Molmoact2 Pretrain | Llama 3 1 8b Instruct |
| Model | MolmoAct2-Pretrain | Llama-3.1-8B-Instruct |
| Version | MolmoAct2-Pretrain | Llama-3.1-8B-Instruct |
| Lifecycle | active | active |
| Released | Unknown | 2024-07-23 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Model-specific input | Text |
| Output modalities | Model-specific input | Text |
| Context window | 16K | 131K |
| Total parameters | 4.9B | 8B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.1 |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | robot_action_generation | chat, generation, tools |
MolmoAct2 Pretrain Capabilities
Llama 3.1 8B Instruct Capabilities
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| vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text | |
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| vs | Seed 2.1 ProByteDance Seed | cross-developer peersimage, text | |
| vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
MolmoAct2 Pretrain vs Llama 3.1 8B Instruct FAQs
Is MolmoAct2 Pretrain or Llama 3.1 8B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both MolmoAct2 Pretrain and Llama 3.1 8B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, MolmoAct2 Pretrain or Llama 3.1 8B Instruct?+
Only Llama 3.1 8B Instruct has a directly sourced input price: $0.050 per million tokens. Only Llama 3.1 8B Instruct has a directly sourced output price: $0.080 per million tokens.
Which has a larger context window, MolmoAct2 Pretrain or Llama 3.1 8B Instruct?+
Llama 3.1 8B Instruct has the larger sourced context window. MolmoAct2 Pretrain supports 16K and Llama 3.1 8B Instruct supports 131K.
Which performs better in benchmarks, MolmoAct2 Pretrain or Llama 3.1 8B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can MolmoAct2 Pretrain or Llama 3.1 8B Instruct be self-hosted?+
Both models have the same recorded self-hosting status: supported. MolmoAct2 Pretrain is open weight; Llama 3.1 8B Instruct is open weight.
Can MolmoAct2 Pretrain and Llama 3.1 8B Instruct understand images?+
MolmoAct2 Pretrain is not documented with image input; Llama 3.1 8B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, MolmoAct2 Pretrain or Llama 3.1 8B Instruct?+
Neither has a larger sourced maximum output. MolmoAct2 Pretrain is — and Llama 3.1 8B Instruct is —.
Do MolmoAct2 Pretrain and Llama 3.1 8B Instruct support reasoning and tool use?+
MolmoAct2 Pretrain: none of these features are definitively sourced. Llama 3.1 8B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, MolmoAct2 Pretrain or Llama 3.1 8B Instruct?+
MolmoAct2 Pretrain has 0 sourced provider routes; Llama 3.1 8B Instruct has 2, so Llama 3.1 8B Instruct has broader tracked availability.
Which offers better value, MolmoAct2 Pretrain or Llama 3.1 8B 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.