Standard Compute
Model comparison 2026

DeepSeek R1 (0528) vs Muse Spark Open

DeepSeek R1 (0528)
DeepSeek · Balanced · Open weights
$0.99/1M · 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 R1
Muse Spark Open
Maker
DeepSeek
Meta
Blended price / 1M
$0.99
$2.15
Input / output
$0.50 in · $2.15 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
4.0
7.0
Agentic Ability
4.4
5.0
Speed
5.1
7.0
Value for Money
4.7
5.3
Reliability
8.4
8.0

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

Verdict: DeepSeek R1 or Muse Spark Open?

Updated 2026-10-16

Choose DeepSeek R1 (0528) if you want research and comparisons that need the original R1 reasoning model. 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, Muse Spark Open leads in 4 of five dimensions (output quality, agentic ability, speed and value for money), while DeepSeek R1 (0528) leads in 1. On price, DeepSeek R1 (0528) runs about $0.99 per 1M tokens (blended) and is open-weight; Muse Spark Open is about $2.15 and open-weight.

Where DeepSeek R1 falls short
  • Slow (about 28 output tokens/s, per Artificial Analysis)
  • 164K context, smaller than 27.9% of the coding-agent requests we measured
Full DeepSeek R1 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 →

The agent matters as much as the model. Compare AI agents →

Current Claude, GPT, Gemini and open models run on Standard Compute

Standard Compute

Current frontier models Claude Fable 5.1 · GPT-6 Astra

More intelligence
per dollar.

Each step goes to the cheapest model that can do it, on providers we vet for price, uptime and speed, with automatic fallback when one fails.

Compliance
Opt-in rules keep a company, team or key on European or US providers
Privacy
We don’t train on your prompts or store them

Fixed monthly plans from $19 · cancel anytime

7-day fair refund Free trial, no card Billing by Stripe3,400+ agent users

Related comparisons