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Model comparison

DeepSeek V4 Pro vs Muse Spark 1.2

DeepSeek against Meta, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

DeepSeek

DeepSeek V4 Pro

Frontier · Open weights

vs

Meta

Muse Spark 1.2

Frontier

AI model capability comparison
SpecificationDeepSeek V4 ProMuse Spark 1.2
ProviderDeepSeekMeta
TierFrontierFrontier
Context window1MWinner: 1.05M
Max output384KNot verifiedUnverified
Input / 1M tokens$1.32Winner: $1.25
Output / 1M tokensWinner: $3.96$4.25
WeightsOpenClosed
Parameters1.6T total / 49B active (MoE)Meta frontier model
Reasoning levelslow, high, maxminimal, low, medium, high, xhigh, max
Modalitiestexttext, image, video, audio, pdf
LicenseMITNot disclosedUnverified
API model iddeepseek-v4-promuse-spark-1.2
ReleasedApril 24, 2026August 5, 2026
Artificial Analysis Intelligence Index [max] (2026-09-26)36Not verifiedUnverified
Artificial Analysis Intelligence Index [xhigh] (2026-09-26)Not verifiedUnverified39.6
SWE-bench Verified (2026-04-24)80.6Not verifiedUnverified
GPQA Diamond (2026-04-24)90.1Not verifiedUnverified
Humanity's Last Exam (2026-04-24)48.2Not verifiedUnverified
MMLU-Pro (2026-04-24)87.5Not verifiedUnverified

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • 2026-09-26
    Artificial Analysis

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.

  • 2026-04-24
    DeepSeek V4 Pro HF model card (vendor, Think Max, exact match)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers

DeepSeek V4 Pro: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Cache hit $0.044 peak / $0.022 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC. Open weights (MIT). 1.6T/49B active MoE.

Muse Spark 1.2: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.

FrontierOpen weightsRecord checked September 26, 2026

DeepSeek V4 Pro

DeepSeek V4 Pro is DeepSeek's large open-weight (MIT) MoE with a 1M-token context — DeepSeek says the cheaper V4.1-Flash now beats it.

Best for

  • Cost-sensitive hosted agents
  • Open-weight deployments
  • High-volume coding

Watch out

On 2026-09-10 DeepSeek said it is phasing out V4-Pro (it later reversed the 2026-09-14 re-route and still serves it); plan new work on V4.1-Flash. Self-hosting needs datacentre VRAM.

FrontierRecord checked September 26, 2026

Muse Spark 1.2

Muse Spark 1.2 is Meta's closed frontier model until the 1.3 refresh (2026-09-02) — still served and ranked in the LMArena text top 10 as of mid-2026.

Best for

  • Frontier reasoning
  • Meta ecosystem
  • Agentic work

Watch out

Closed-weights (unlike Llama 4); Meta's post-Llama-4 frontier branding.

When the cheaper one wins

DeepSeek V4 Pro is cheaper on output at $3.96 per million tokens against $4.25 for Muse Spark 1.2 — about 1.1×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, DeepSeek V4 Pro is 62.8% Pass@1 at $0.24/task versus Muse Spark 1.2 at 54.9% / $3.7/task. These are standard-tier API rates, excluding batch and cache discounts.

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Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 4/5 core specs verified on both sides — Not published for at least one side: max output.
  • 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
  • Verified within the last 90 days — Newest catalog check was 2 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? DeepSeek V4 Pro · Muse Spark 1.2

Common questions

DeepSeek V4 Pro vs Muse Spark 1.2

Answered from the verified figures on this page rather than general guidance.

Is DeepSeek V4 Pro or Muse Spark 1.2 cheaper for input?
Muse Spark 1.2 is cheaper at $1.25 per million input tokens, against $1.32 for DeepSeek V4 Pro — roughly 1.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4 Pro has tiered pricing: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Cache hit $0.044 peak / $0.022 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC. Open weights (MIT). 1.6T/49B active MoE. Muse Spark 1.2 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.
Is DeepSeek V4 Pro or Muse Spark 1.2 cheaper for output?
DeepSeek V4 Pro is cheaper at $3.96 per million output tokens, against $4.25 for Muse Spark 1.2 — roughly 1.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; DeepSeek V4 Pro has tiered pricing: Official peak rates $1.32/$3.96 per MTok; off-peak $0.66/$1.98 (cache-miss). Cache hit $0.044 peak / $0.022 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC. Open weights (MIT). 1.6T/49B active MoE. Muse Spark 1.2 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.
Which has the larger context window, DeepSeek V4 Pro or Muse Spark 1.2?
Muse Spark 1.2 accepts 1.05M tokens against 1M for DeepSeek V4 Pro. This only matters if you routinely send very long documents or large codebases.
Do DeepSeek V4 Pro and Muse Spark 1.2 support the same reasoning levels?
DeepSeek V4 Pro exposes low, high, max, while Muse Spark 1.2 exposes minimal, low, medium, high, xhigh, max.
Should I use DeepSeek V4 Pro or Muse Spark 1.2?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. DeepSeek V4 Pro suits cost-sensitive hosted agents; Muse Spark 1.2 suits frontier reasoning.
Can I self-host DeepSeek V4 Pro or Muse Spark 1.2?
DeepSeek V4 Pro publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Muse Spark 1.2 is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: Muse Spark 1.2 costs $1.25 per 1M tokens versus $1.32 for DeepSeek V4 Pro — a 1.1x difference at the headline tier.
  • Context: Muse Spark 1.2 takes 1.05M against 1M for DeepSeek V4 Pro — only decisive if your prompts approach the smaller window.
  • Deployment: DeepSeek V4 Pro publishes weights you can self-host; the other is API-only.
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