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Devstral Small 1.1 vs Muse Spark 1.3

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 9.6. Muse Spark 1.3 is 1.2x cheaper per token.

Mistral AI · Meta · Updated for 2026

Which is better?

Muse Spark 1.3 leads the overall LLM Stats Score 55.3 to 9.6, ranking #5 overall.

On price, Muse Spark 1.3 is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Muse Spark 1.3 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Devstral Small 1.1

  • you need open weights you can self-host or fine-tune

Choose Muse Spark 1.3

  • overall performance matters — it scores 55.3 and ranks #5 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 1.2x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Core performance indexes
9.6
#258
55.3
#5
9.5
#250
52.8
#7
1.1
#227
41.7
#10
Cost, coverage & limits
Benchmark wins
Input price
$0.10 / M
$0.10 / M
Output price
$0.30 / M
$0.20 / M
Context window
128,000
1,048,576

Individual benchmarks

1 reported for Devstral Small 1.1 · 11 for Muse Spark 1.3

No common benchmarks found

Devstral Small 1.1 and Muse Spark 1.3don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Muse Spark 1.3 costs less

For input processing, Devstral Small 1.1 ($0.10/1M tokens) costs the same as Muse Spark 1.3 ($0.10/1M tokens).

For output processing, Devstral Small 1.1 ($0.30/1M tokens) is 1.5x more expensive than Muse Spark 1.3 ($0.20/1M tokens).

In conclusion, Devstral Small 1.1 is more expensive than Muse Spark 1.3.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Fri Sep 04 2026 • llm-stats.com
Mistral AI
Devstral Small 1.1
Input tokens$0.10
Output tokens$0.30
Best providerMistral
Meta
Muse Spark 1.3
Input tokens$0.10
Output tokens$0.20
Best providerMeta
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

Muse Spark 1.3 accepts 1,048,576 input tokens compared to Devstral Small 1.1's 128,000 tokens. Muse Spark 1.3 can generate longer responses up to 943,718 tokens, while Devstral Small 1.1 is limited to 128,000 tokens.

Mistral AI
Devstral Small 1.1
Input128,000 tokens
Output128,000 tokens
Meta
Muse Spark 1.3
Input1,048,576 tokens
Output943,718 tokens
Fri Sep 04 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Spark 1.3 supports multimodal inputs, whereas Devstral Small 1.1 does not.

Muse Spark 1.3 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Devstral Small 1.1

Text
Images
Audio
Video

Muse Spark 1.3

Text
Images
Audio
Video

License

Usage and distribution terms

Devstral Small 1.1 is licensed under Apache 2.0, while Muse Spark 1.3 uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

Devstral Small 1.1

Apache 2.0

Open weights

Muse Spark 1.3

Proprietary

Closed source

Release Timeline

When each model was launched

Devstral Small 1.1 was released on 2025-07-11, while Muse Spark 1.3 was released on 2026-09-02.

Muse Spark 1.3 is 14 months newer than Devstral Small 1.1.

Devstral Small 1.1

Jul 11, 2025

1.2 years ago

Muse Spark 1.3

Sep 2, 2026

2 days ago

1.1yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Devstral Small 1.1 is available from Mistral AI. Muse Spark 1.3 is available from Meta Model API.

Devstral Small 1.1

mistral logo
Mistral
Input Price:Input: $0.10/1MOutput Price:Output: $0.30/1M

Muse Spark 1.3

meta logo
Meta
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Devstral Small 1.1 and Muse Spark 1.3 side-by-side, then vote on the output you prefer.

Devstral Small 1.1
✓ Preferred
Muse Spark 1.3
Open in Playground

FAQ

Common questions about Devstral Small 1.1 vs Muse Spark 1.3.

Which is better, Devstral Small 1.1 or Muse Spark 1.3?

Muse Spark 1.3 leads the LLM Stats Score 55.3 to 9.6. Devstral Small 1.1 is made by Mistral AI and Muse Spark 1.3 is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Devstral Small 1.1 compare to Muse Spark 1.3 in benchmarks?

Devstral Small 1.1 scores SWE-Bench Verified: 53.6%. Muse Spark 1.3 scores MRCR v2 (8-needle): 98.5%, MRCR v2 (8-needle, 512K-1M): 98.1%, DeepSearchQA: 89.4%, Terminal-Bench 2.1: 88.8%, DeepSWE 1.1: 75.4%.

Is Devstral Small 1.1 cheaper than Muse Spark 1.3?

Both models cost $0.10 per million input tokens.

What are the context window sizes for Devstral Small 1.1 and Muse Spark 1.3?

Devstral Small 1.1 supports 128K tokens and Muse Spark 1.3 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Devstral Small 1.1 and Muse Spark 1.3?

Key differences include LLM Stats Score (9.6 vs 55.3), context window (128K vs 1.0M), multimodal support (no vs yes), licensing (Apache 2.0 vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes Devstral Small 1.1 and Muse Spark 1.3?

Devstral Small 1.1 is developed by Mistral AI and Muse Spark 1.3 is developed by Meta.