DeepSeek V4 Pro 0813 vs Bonsai 1.7B
At a Glance
| Compare |  DeepSeek V4 Pro 0813DeepSeek |  Bonsai 1.7BPrismML |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #20 of 4665.0 score · 3/3 sources · complete | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #20 of 44$0.094 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #14 of 3857.4 score · 3/3 sources · complete | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.30Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $2.60Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,049K | 33K |
| Model facts checked | Sep 3, 2026View model evidence → | Sep 18, 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 | DeepSeek-V4-Pro-0813 | Bonsai 1.7B |
|---|---|---|
| Developer | DeepSeek | PrismML |
| Family | Deepseek V4 Pro | Bonsai 1 7b |
| Model | DeepSeek-V4-Pro-0813 | Bonsai 1.7B |
| Version | DeepSeek-V4-Pro-0813 | Bonsai 1.7B |
| Lifecycle | active | active |
| Released | 2026-08-13 | 2026-03-29 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 1,049K | 33K |
| Total parameters | 1.6T | 1.7B |
| Active parameters | 49B | Unknown |
| License | mit | apache-2.0 |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard) | Unknown |
| Capabilities | agents, chat, coding, generation, reasoning, tools | chat, generation |
| Effective bit width | Unknown | 1 bit per weight |
| Weight size | Unknown | 0.25 GB |
| Weight format | Unknown | Binary Q1_0 |
DeepSeek V4 Pro 0813 Capabilities
Bonsai 1.7B Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-Pro-0813DeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4-Pro-0813DeepSeek | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V4-Pro-0813DeepSeek | vs | Grok 4.6xAI | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V4-Pro-0813DeepSeek | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4-Pro-0813DeepSeek | cross-developer peerstext |
DeepSeek-V4-Pro-0813DeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Pro 0813 vs Bonsai 1.7B FAQs
Is DeepSeek V4 Pro 0813 or Bonsai 1.7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro 0813 and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
Only DeepSeek V4 Pro 0813 has a directly sourced input price: $1.30 per million tokens. Only DeepSeek V4 Pro 0813 has a directly sourced output price: $2.60 per million tokens.
Which has a larger context window, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
DeepSeek V4 Pro 0813 has the larger sourced context window. DeepSeek V4 Pro 0813 supports 1,049K and Bonsai 1.7B supports 33K.
Which performs better in benchmarks, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4 Pro 0813 or Bonsai 1.7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4 Pro 0813 is open weight; Bonsai 1.7B is open weight.
Can DeepSeek V4 Pro 0813 and Bonsai 1.7B understand images?+
DeepSeek V4 Pro 0813 is not documented with image input; Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
Neither has a larger sourced maximum output. DeepSeek V4 Pro 0813 is — and Bonsai 1.7B is —.
Do DeepSeek V4 Pro 0813 and Bonsai 1.7B support reasoning and tool use?+
DeepSeek V4 Pro 0813: reasoning and tool calling. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
DeepSeek V4 Pro 0813 has 3 sourced provider routes; Bonsai 1.7B has 0, so DeepSeek V4 Pro 0813 has broader tracked availability.
Which offers better value, DeepSeek V4 Pro 0813 or Bonsai 1.7B?+
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.