Standard Compute
Model comparison 2026

DeepSeek V3.2 vs Muse Spark Open

DeepSeek V3.2
DeepSeek · Balanced · Open weights
$0.42/1M · 164K ctx
Muse Spark Open
Meta · Balanced · Open weights
$2.15/1M · 1.0M ctx

Community vote

Pick a winner in each dimension. You can change your vote any time.

Output Quality

Correctness and depth of what it produces

Agentic Ability

Tool calls, instruction-following, and multi-step tasks

Speed

Tokens per second and time-to-first-token

Value for Money

How much capability you get per dollar

Reliability

Consistent results: fewer refusals, loops and format breaks

Specs and pricing side by side

Spec
DeepSeek V3.2
Muse Spark Open
Maker
DeepSeek
Meta
Blended price / 1M
$0.42
$2.15
Input / output
$0.26 in · $0.80 out / 1M tokens
$1.25 in · $4.25 out / 1M tokens
Context window
164K
1.0M
Open weights
Yes
Yes
Tool use
Yes
Yes
Reasoning
Yes
Yes
Output Quality
5.2
7.0
Agentic Ability
4.7
5.0
Speed
7.8
7.0
Value for Money
6.3
5.3
Reliability
8.4
8.0

Pricing and capabilities synced from the OpenRouter catalogue. Scores are editorial (0–10).

Verdict: DeepSeek V3.2 or Muse Spark Open?

Updated 2026-10-16

Choose DeepSeek V3.2 if you want existing V3.2 setups with short prompts. Choose Muse Spark Open if you want trying Meta's open-weight coding model through OpenRouter, or self-hosting it where code must stay on your own hardware.

In our editorial scoring, DeepSeek V3.2 leads in 3 of five dimensions (speed, value for money and reliability), while Muse Spark Open leads in 2. On price, DeepSeek V3.2 runs about $0.42 per 1M tokens (blended) and is open-weight; Muse Spark Open is about $2.15 and open-weight.

Where DeepSeek V3.2 falls short
  • 164K context, smaller than 27.9% of the coding-agent requests we measured
  • Superseded by DeepSeek V4.1 Flash, which scores higher for less
Full DeepSeek V3.2 breakdown →
Where Muse Spark Open falls short
  • No independent agent benchmark results yet; the published figures are Meta's own
  • Self-hosting needs at least 700 GB of GPU memory in NVFP4 (2 TB in BF16), per Meta's model card
Full Muse Spark Open breakdown →

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