Seed 2.0 Code vs Llama 3.3 70B Instruct
At a Glance
| Compare | Seed 2.0 CodeByteDance Seed | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.50Openrouter ↗ · Sep 22, 2026 | $0.10Openrouter ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $3.00Openrouter ↗ · Sep 22, 2026 | $0.32Openrouter ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | Not reported | 131K |
| Model facts checked | Sep 3, 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 | Seed 2.0 Code | Llama-3.3-70B-Instruct |
|---|---|---|
| Developer | ByteDance Seed | Meta |
| Family | Seed 2 0 | Llama 3 3 70b Instruct |
| Model | Seed 2.0 Code | Llama-3.3-70B-Instruct |
| Version | Seed 2.0 Code | Llama-3.3-70B-Instruct |
| Lifecycle | active | active |
| Released | 2026-02-14 | 2024-12-06 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | Unknown | 131K |
| Total parameters | Unknown | 70.6B |
| Active parameters | Unknown | Unknown |
| License | Unknown | llama3.3 |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Openrouter (Standard) | Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | agents, chat, generation, reasoning, structured_outputs, tools | chat, generation, tools |
Seed 2.0 Code Capabilities
Llama 3.3 70B Instruct Capabilities
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Primary Evidence
Sources and Freshness
Questions
Seed 2.0 Code vs Llama 3.3 70B Instruct FAQs
Is Seed 2.0 Code or Llama 3.3 70B Instruct better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Seed 2.0 Code and Llama 3.3 70B Instruct, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Seed 2.0 Code or Llama 3.3 70B Instruct?+
Seed 2.0 Code is $0.50 and Llama 3.3 70B Instruct is $0.10 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric. Seed 2.0 Code is $3.00 and Llama 3.3 70B Instruct is $0.32 per million tokens, so Llama 3.3 70B Instruct is cheaper on this metric.
Which has a larger context window, Seed 2.0 Code or Llama 3.3 70B Instruct?+
Neither model has a larger sourced context window in this comparison. Seed 2.0 Code is — and Llama 3.3 70B Instruct is 131K.
Which performs better in benchmarks, Seed 2.0 Code or Llama 3.3 70B Instruct?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Seed 2.0 Code or Llama 3.3 70B Instruct be self-hosted?+
Llama 3.3 70B Instruct is the only model in this pair currently marked as self-hostable. Seed 2.0 Code is not marked open weight; Llama 3.3 70B Instruct is open weight.
Can Seed 2.0 Code and Llama 3.3 70B Instruct understand images?+
Seed 2.0 Code is not documented with image input; Llama 3.3 70B Instruct is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Seed 2.0 Code or Llama 3.3 70B Instruct?+
Neither has a larger sourced maximum output. Seed 2.0 Code is — and Llama 3.3 70B Instruct is —.
Do Seed 2.0 Code and Llama 3.3 70B Instruct support reasoning and tool use?+
Seed 2.0 Code: reasoning and tool calling. Llama 3.3 70B Instruct: tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Seed 2.0 Code or Llama 3.3 70B Instruct?+
Seed 2.0 Code has 1 sourced provider route; Llama 3.3 70B Instruct has 3, so Llama 3.3 70B Instruct has broader tracked availability.
Which offers better value, Seed 2.0 Code or Llama 3.3 70B 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.