DeepSeek V4.1 Flash vs Muse Spark 1.3

At a glance

• Capability index

  • DeepSeek V4.1 Flash155.6
  • Muse Spark 1.3156.8

  Muse Spark 1.3 is 1.2 points higher.

• Price per 1M tokens

  • DeepSeek V4.1 Flash$0.464
  • Muse Spark 1.3$2.00

  DeepSeek V4.1 Flash is 4.3 times cheaper.

• Context window

  • DeepSeek V4.1 Flash1M tokens
  • Muse Spark 1.31M tokens

  They take in about the same.

• Released

  • DeepSeek V4.1 FlashSep 10, 2026
  • Muse Spark 1.3Sep 2, 2026

  DeepSeek V4.1 Flash is 8 days newer.

Where each scores higher

On the 19 tests both have taken, each on its own scale. A gap of a point or two can sit within a test’s margin of error.

3 more tests have results for only one of them; the table below lists every result.

Everything side by side

Comparison of DeepSeek V4.1 Flash vs Muse Spark 1.3

Measure	DeepSeek V4.1 FlashDeepSeek	Muse Spark 1.3Meta AI
Model
Maker	DeepSeek	Meta AI
Sold here by	engy	Cloudflare AI Gateway
API model ID	deepseek-v4.1-flash	meta/muse-spark-1.3
Released	Sep 10, 2026	Sep 2, 2026
Knowledge cutoff	May 2025	Not reported
Weights	Open: downloadable	Closed: hosted access only
Developer’s country	China	United States
Hosts selling it	80	14
Training compute	4.3 × 10²⁴ FLOPEpoch AI estimate	Not published
Test results
Capability index	155.6#31 of 277	156.8 (best of these models)#21 of 277
LiveBench overall	81.1%#9 of 70	81.6% (best of these models)#7 of 70
Arena text	1475#38 of 415	1494 (best of these models)#11 of 415
LiveBench coding	80.0%#23 of 70	81.1% (best of these models)#17 of 70
Arena WebDev	1619#24 of 142	1659 (best of these models)#12 of 142
Arena coding	1526#27 of 410	1537 (best of these models)#14 of 410
LiveBench mathematics	93.3%#23 of 70	96.0% (best of these models)#13 of 70
Mock AIME 2024–2025	98.3%#24 of 303	99.2% (best of these models)#17 of 303
FrontierMath Tiers 1–3	67.4%#30 of 117	74.4% (best of these models)#21 of 117
FrontierMath Tier 4	26.8%#41 of 73	46.3% (best of these models)#26 of 73
Arena math	1495#22 of 400	1503 (best of these models)#13 of 400
SimpleQA Verified	42.9%#43 of 90	Not tested
LiveBench agentic coding	77.3% (best of these models)#1 of 70	64.1%#9 of 70
CursorBench	Not tested	41.6%#29 of 71
LiveBench language	81.2%#29 of 70	82.8% (best of these models)#25 of 70
LiveBench instructions	70.0%#32 of 70	78.0% (best of these models)#4 of 70
Arena creative writing	1439#61 of 413	1461 (best of these models)#28 of 413
Arena instruction following	1477#24 of 415	1484 (best of these models)#18 of 415
LiveBench data analysis	79.3%#18 of 70	79.6% (best of these models)#13 of 70
LiveBench reasoning	86.7%#32 of 70	89.7% (best of these models)#16 of 70
GPQA Diamond	89.8%#50 of 323	Not tested
Chess puzzles	48.0% (best of these models)#20 of 243	37.0%#46 of 243
Arena hard prompts	1499#36 of 415	1516 (best of these models)#12 of 415
Price per million tokens
Input	$0.04 (best of these models)	$1.25
Output	$0.08 (best of these models)	$4.25
Blended, 3 input to 1 output	$0.05 (best of these models)	$2.00
Cached input	$0.008 (best of these models)	$0.16
Limits
Context window	327.7K tokens	1M tokens (best of these models)
Max input	1M tokens	1M tokens
Max output	65.5K tokens	131.1K tokens (best of these models)
Features
Reads	images and text	audio, images, PDFs, text and video
Produces	text	text
Reasoning	Yeseffort none, low, high, max; can be switched off; thinking budget can be set	Yeseffort minimal, low, medium, high, xhigh
Tool calling	Yes	Yes
Structured output	Yes, such as JSON	Yes, such as JSON
File attachments	Yes	Yes
Temperature setting	Supported	Supported

Bold marks the better value in each row. Prices are each model’s own list price, or the middle price across hosts when the maker does not sell it, blended as three input tokens for every output token. Test results as published by LiveBench (Apache-2.0), Epoch AI (CC BY 4.0), Cursor CursorBench, compiled by Epoch AI (CC BY 4.0), Arena (CC BY 4.0); model details and prices from models.dev (MIT). Confirm pricing with the provider before use.