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Van Data Team

text model comparison

DeepSeek-V4.1-Flash vs Muse Ember

DeepSeek-V4.1-Flash and Muse Ember are both current text models, from DeepSeek and Meta. Muse Ember is 67% cheaper per 1M input tokens than DeepSeek-V4.1-Flash, $0.10 vs $0.30. On Arena WebDev, DeepSeek-V4.1-Flash scores 1664 Elo and Muse Ember 1609 Elo (Arena, measured November 10, 2026). Both accept about 1.05M tokens of context (1,000,000 and 1,048,576).

Higher Arena WebDev
DeepSeek-V4.1-Flash1664 Elo against 1609 Elo
Lower price per 1M input tokens
Muse Ember$0.10 against $0.30
Longer context window
Muse Ember1.05M tokens against 1M tokens
Newer release
Muse EmberNov 2026 against Sep 2026

Data verified November 11, 2026 · Dataset 2026-11-11

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Side by side

DeepSeek-V4.1-Flash and Muse Ember, line by line

Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.

FactDeepSeek-V4.1-FlashMuse Ember
Price
Price per 1M input tokens$0.30$0.10 (better)
Price per 1M output tokens$1.20$0.50 (better)
Independent benchmarks
Arena WebDev1664 Elo (#12 of 44) (better)1609 Elo (#23 of 44)
Arena Text1475 Elo (#16 of 48)Not measured
GPQA Diamond93.2% (#18 of 40)Not measured
FrontierMath Tiers 1-3 (v2)71.2% (#22 of 39)Not measured
SimpleQA Verified42.5% (#26 of 36)Not measured
Specs
ProviderDeepSeekMeta
Release dateSeptember 10, 2026November 9, 2026
AccessAPI and open weightsAPI only
Context window1,000,000 tokens1,048,576 tokens
Max output384,000 tokens524,288 tokens
Inputtext, imagetext, image, audio, video
Outputtexttext

Scores:Arena · 2026-11-11Epoch AI · 2026-11-11

Open both in the interactive comparison

Charts

Against every current text model

Where DeepSeek-V4.1-Flash and Muse Ember land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

On Arena WebDev, DeepSeek-V4.1-Flash ranks #12 and Muse Ember #23 of 44 current models. Each bar is the share of current models it beats: on score, price and context.

DeepSeek-V4.1-Flash and Muse Ember among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

DeepSeek-V4.1-Flash:#16of 48

1,475 Elo · ahead of 66%

Muse Ember: Not scored

Arena WebDev

DeepSeek-V4.1-Flash:#12of 44

1,664 Elo · ahead of 74%

Muse Ember:#23of 44

1,609 Elo · ahead of 47%

GPQA Diamond

DeepSeek-V4.1-Flash:#18of 40

93.2% · ahead of 56%

Muse Ember: Not scored

FrontierMath Tiers 1-3 (v2)

DeepSeek-V4.1-Flash:#22of 39

71.2% · ahead of 45%

Muse Ember: Not scored

SimpleQA Verified

DeepSeek-V4.1-Flash:#26of 36

42.5% · ahead of 29%

Muse Ember: Not scored

Price and context

Price per 1M input tokens

DeepSeek-V4.1-Flash:#13of 57

$0.30 · cheaper than 71%

Muse Ember:#1of 57

$0.10 · cheaper than 95%

Price per 1M output tokens

DeepSeek-V4.1-Flash:#13of 57

$1.20 · cheaper than 73%

Muse Ember:#3of 57

$0.50 · cheaper than 91%

Context window

DeepSeek-V4.1-Flash:#29of 67

1M tokens · longer than 24%

Muse Ember:#12of 67

1.05M tokens · longer than 64%

Scores fromArena · 2026-11-11Epoch AI · 2026-11-11

Quality against price

Arena Text · measured November 9, 2026

DeepSeek-V4.1-Flash scores 1,475 at $0.525 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.

Quality against priceArena Text score against price for 40 text models, DeepSeek-V4.1-Flash and Muse Ember marked.1,2501,3001,3501,4001,4501,5001,550$0.30$1$3$10MOST ATTRACTIVE QUADRANTMedian price $2.08Median 1,472Blended price per 1M tokens (3 input : 1 output), log scaleArena Text (Elo)DeepSeek-V4.1-Flash
Quality against price: the data
Arena Text score against price for 40 text models, DeepSeek-V4.1-Flash and Muse Ember marked.
ModelProviderBlended price per 1M tokensArena Text (Elo)
Gemini 4 CarbonGoogle$41,526
Gemini 4 ArgonGoogle$41,522
Muse WonderMeta$81,511
Claude Opus 5.5Anthropic$81,506
Gemini 3.8 FlashGoogle$1.501,497
Muse Spark 1.3Meta$21,496
Kimi K3Moonshot Kimi$61,487
GPT-5.6 SolOpenAI$81,485
Qwen3.8-MaxAlibaba Qwen$31,484
Gemini 3.6 FlashGoogle$31,483
GLM-5.3Z.ai$2.151,483
GPT-6.1 SolOpenAI$41,482
GPT-5.5OpenAI$111,481
MiMo-V2.6-ProXiaomi MiMo$0.541,480
GPT-5.4OpenAI$5.631,475
DeepSeek-V4.1-FlashDeepSeek$0.521,475
GPT-6 AstraOpenAI$201,474
GLM-5.3-FlashZ.ai$0.241,474
Gemini 3 Flash PreviewGoogle$1.131,473
Grok 4.20xAI$1.561,472
Claude Sonnet 5.5Anthropic$41,472
GPT-5.6 TerraOpenAI$4.501,468
Grok 4.5xAI$31,466
DeepSeek-V4-ProDeepSeek$1.981,464
GPT-6 SolOpenAI$41,457
Grok 4.6xAI$31,456
Gemini 3.5 Flash-LiteGoogle$0.851,455
GPT-5.6 LunaOpenAI$0.451,453
MiMo-V2.6-FlashXiaomi MiMo$0.181,448
Step 5 PreviewStepFun$1.431,448
Grok 4.3xAI$1.561,442
Grok 4.7xAI$31,442
GPT-6 LunaOpenAI$0.201,441
Mistral Large 4Mistral AI$1.051,423
Claude Haiku 5.5Anthropic$0.201,422
GPT-4.1OpenAI$3.501,415
Inkling 2 SmallThinking Machines Lab$0.471,412
Solar Pro 4Upstage$0.521,387
GPT-4.1 miniOpenAI$0.701,383
GPT-4.1 nanoOpenAI$0.181,322

Cost

What each costs

Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.

The same work, priced

For 10M input and 2M output tokens, DeepSeek-V4.1-Flash costs 2.7 times as much as Muse Ember, at each provider's listed rates.

What DeepSeek-V4.1-Flash and Muse Ember cost for the same work, at the rates each provider lists.

  • 10M input and 2M output tokens

    DeepSeek-V4.1-Flash costs 2.7× as much

    DeepSeek-V4.1-Flash
    $5.40
    Muse Ember
    $2

  • The same, with 9M of the input tokens cached

    DeepSeek-V4.1-Flash costs 2.4× as much

    DeepSeek-V4.1-Flash
    $2.75
    Muse Ember
    $1.14
Workload or rateDeepSeek-V4.1-FlashMuse Ember
Example workloads
10M input and 2M output tokens$5.40$2 (better)
The same, with 9M of the input tokens cached$2.75$1.14 (better)
Other listed rates
1M input tokens, cached$0.006$0.004 (better)
1M input tokens, cached, prompts over 200K tokensNot listed$0.012
1M input tokens, off peak$0.15Not listed
1M output tokens, off peak$0.60Not listed
1M input tokens, off-peak, cached$0.003Not listed
1M input tokens, prompts over 200K tokensNot listed$0.30
1M output tokens, prompts over 200K tokensNot listed$1.50

Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.

Launch price $100, regular price $150

Book the cost audit

Verdict

When to choose each

DeepSeek-V4.1-Flash

Where it leads

  • Higher Arena WebDev score, 1664 Elo vs 1609 Elo
  • Open weights you can run yourself

Best for

  • High-volume agent and multimodal workloads where per-token cost dominates
  • Teams that want a frontier-adjacent model they can also self-host under MIT terms

Watch out for

  • The recorded API rates are the peak-hour prices; DeepSeek charges half outside 01:00-04:00 and 06:00-10:00 UTC on weekdays
  • Running the open weights moves cost to your own infrastructure, which the token rates here do not represent
  • Epoch AI and Arena WebDev scored build 1017, which the API serves; Arena Text has rated only the September build so far
Full DeepSeek-V4.1-Flash profile

Muse Ember

Where it leads

  • Lower input and output prices, $0.10 and $0.50 vs $0.30 and $1.20 per 1M tokens
  • Longer responses, up to 524,288 vs 384,000 output tokens
  • Accepts audio and video input
  • Cheaper cached input, $0.004 vs $0.006 per 1M tokens

Best for

  • Sub-agent work under a larger planner such as Muse Spark or Muse Wonder
  • High-volume extraction, classification and routing over images, documents and records

Watch out for

  • A request over 200K input tokens is billed in full at three times the rate: $0.30 input, $1.50 output and $0.012 cached input per 1M tokens
  • Claude Haiku 5.5 scores higher on FrontierCode 2.0 as run by Artificial Analysis (34.9% against 27.2%), a row in Meta's own launch post
  • Arena Text has not rated it yet, and its WebDev rating rests on 1,337 votes so far
Full Muse Ember profile

From benchmark to production

Building on DeepSeek-V4.1-Flash or Muse Ember?

Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.

AI Model Cost AuditLaunch price $100, regular price $150What the audit includes

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FAQ

DeepSeek-V4.1-Flash vs Muse Ember FAQ

Which is cheaper, DeepSeek-V4.1-Flash or Muse Ember?

Muse Ember is 67% cheaper per 1M input tokens than DeepSeek-V4.1-Flash, $0.10 vs $0.30. For 10M input and 2M output tokens, DeepSeek-V4.1-Flash costs $5.40 and Muse Ember $2 at standard rates.

Which is cheaper with prompt caching, DeepSeek-V4.1-Flash or Muse Ember?

DeepSeek-V4.1-Flash charges $0.006 per 1M cached input tokens, 98% below its standard input rate, and Muse Ember $0.004, 96% below. With 9M of 10M input tokens cached and 2M output tokens, DeepSeek-V4.1-Flash costs $2.75 and Muse Ember $1.14.

Which scores higher on independent benchmarks, DeepSeek-V4.1-Flash or Muse Ember?

DeepSeek-V4.1-Flash leads on Arena WebDev, 1664 Elo to 1609 Elo. Scores are independent evaluations, not either vendor's launch figures.

Which has the larger context window, DeepSeek-V4.1-Flash or Muse Ember?

Both accept about 1.05M tokens of context (1,000,000 and 1,048,576). Muse Ember returns up to 524,288 tokens per response, DeepSeek-V4.1-Flash 384,000.

How do DeepSeek-V4.1-Flash and Muse Ember differ in features?

DeepSeek-V4.1-Flash takes text and image input; Muse Ember takes text, image, audio and video.

Can I run DeepSeek-V4.1-Flash or Muse Ember myself?

DeepSeek-V4.1-Flash is released as open weights you can run yourself; Muse Ember is available through its provider's API only.

Further reading

More on DeepSeek-V4.1-Flash and Muse Ember

Articles on DeepSeek-V4.1-Flash and Muse Ember

Related comparisons

Production evaluation

Choosing between DeepSeek-V4.1-Flash and Muse Ember?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty
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