โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Muse Spark 1.1 vs o1

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Muse Spark 1.1 and o1.

Meta

Muse Spark 1.1

Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context...

View Full Specs
OpenAI

o1

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

View Full Specs

Technical Specifications

SpecificationMuse Spark 1.1o1
ProviderMetaOpenAI
Context Window1,048,576 tokens200,000 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A2500 ms
Deployment Modelmanaged apimanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-07-162024-12-17

API Pricing Comparison

Input Price per Million Tokens

Muse Spark 1.1

$1.25

o1

$15.00

Output Price per Million Tokens

Muse Spark 1.1

$4.25

o1

$60.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
N/Avs9180.0%
Muse Spark 1.1
o1
HumanEvalPython coding & logic synthesis
N/Avs9450.0%
Muse Spark 1.1
o1
MATHComplex mathematical problem solving
N/Avs9480.0%
Muse Spark 1.1
o1
GPQAGraduate-level expert reasoning
N/Avs7830.0%
Muse Spark 1.1
o1
HellaSwagCommonsense reasoning and inference
N/Avs9200.0%
Muse Spark 1.1
o1
MT-BenchMulti-turn conversation flow quality
N/Avs940.0%
Muse Spark 1.1
o1

Muse Spark 1.1 Quirks & Gotchas

No developer gotchas reported.

o1 Quirks & Gotchas

  • โ–ธReasoning model โ€” high latency by design, not for real-time use
  • โ–ธBest for complex math/code reasoning where accuracy > speed
  • โ–ธUse o3-mini when you need reasoning with lower latency