Llama 4 Maverick 17B 128E Instruct vs Hunyuan3D 2.1
At a Glance
| Compare |  Hunyuan3D 2.1Tencent | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.1875Openrouter ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $0.6525Openrouter ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,000K | Not reported |
| 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-4-Maverick-17B-128E-Instruct | Hunyuan3D-2.1 |
|---|---|---|
| Developer | Meta | Tencent |
| Family | Llama 4 Maverick 17b 128e Instruct | Hunyuan3d 2 1 |
| Model | Llama-4-Maverick-17B-128E-Instruct | Hunyuan3D-2.1 |
| Version | Llama-4-Maverick-17B-128E-Instruct | Hunyuan3D-2.1 |
| Lifecycle | active | active |
| Released | 2025-04-05 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Image |
| Output modalities | Text | 3D |
| Context window | 1,000K | Unknown |
| Total parameters | 401.6B | Unknown |
| Active parameters | 17B | Unknown |
| License | other | other |
| Open weights | Yes | Yes |
| API available | Yes | Unknown |
| Self-hostable | Yes | Yes |
| Provider access | Openrouter (Standard) | Unknown |
| Capabilities | chat, generation, tools | generation |
Llama 4 Maverick 17B 128E Instruct Capabilities
Hunyuan3D 2.1 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Maverick 17B 128E Instruct vs Hunyuan3D 2.1 FAQs
Is Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Maverick 17B 128E Instruct and Hunyuan3D 2.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
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 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
Neither model has a larger sourced context window in this comparison. Llama 4 Maverick 17B 128E Instruct is 1,000K and Hunyuan3D 2.1 is —.
Which performs better in benchmarks, Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Maverick 17B 128E Instruct is open weight; Hunyuan3D 2.1 is open weight.
Can Llama 4 Maverick 17B 128E Instruct and Hunyuan3D 2.1 understand images?+
Llama 4 Maverick 17B 128E Instruct is documented with image input; Hunyuan3D 2.1 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
Neither has a larger sourced maximum output. Llama 4 Maverick 17B 128E Instruct is — and Hunyuan3D 2.1 is —.
Do Llama 4 Maverick 17B 128E Instruct and Hunyuan3D 2.1 support reasoning and tool use?+
Llama 4 Maverick 17B 128E Instruct: tool calling and image input. Hunyuan3D 2.1: image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
Llama 4 Maverick 17B 128E Instruct has 1 sourced provider route; Hunyuan3D 2.1 has 0, so Llama 4 Maverick 17B 128E Instruct has broader tracked availability.
Which offers better value, Llama 4 Maverick 17B 128E Instruct or Hunyuan3D 2.1?+
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.