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Model comparison

DeepSeek V4-Pro vs Muse Glimmer 30B

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

Catalog record checked August 14, 2026; individual provider fields may change.

DeepSeek

DeepSeek V4-Pro

Balanced · Open weights

vs

Meta

Muse Glimmer 30B

Balanced · Open weights

AI model capability comparison
SpecificationDeepSeek V4-ProMuse Glimmer 30B
ProviderDeepSeekMeta
TierBalancedBalanced
Context windowWinner: 1M131K
Max output384KNot verified
Input / 1M tokens$0.435Winner: $0
Output / 1M tokens$0.87Winner: $0
WeightsOpenOpen
Parameters1.6T total / 49B active (MoE)~29.6B dense (incl. ~1.8B perception encoder)
Reasoning levelslow, high, maxNot verified
Modalitiestexttext, image
ReleasedApril 24, 2026August 10, 2026
Artificial Analysis Intelligence Index [max] (2026-08-14)53Not verified

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.

Pricing tiers: DeepSeek V4-Pro: API id `deepseek-v4-pro` is unchanged across the 2026-04-24 preview and the 2026-08-13 GA checkpoint. Current list is $0.435/$0.87 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.66/$1.98, peak $1.32/$3.96 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. · Muse Glimmer 30B: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.

BalancedOpen weights

DeepSeek V4-Pro

DeepSeek's larger V4 sibling for harder reasoning and coding, still priced well below closed mid tiers.

Best for

  • Harder reasoning on a budget
  • Open-weight deployments
  • Agentic coding when Flash is not enough

Watch out

The April preview and 2026-08-13 GA share one API id — pin the date of any score you cite. Artificial Analysis currently evaluates the 0813 (max) checkpoint at Intelligence Index 53, Terminal-Bench v2.1 78.7%, and GPQA Diamond 92.8%. Peak/off-peak API rates take effect 2026-08-16 16:00 UTC.

BalancedOpen weights

Muse Glimmer 30B

Meta's open-weight multimodal agentic model distilled from Muse Spark for local consumer hardware (~24–32GB class with 4-bit).

Best for

  • Local agents
  • On-device coding + tool use
  • Privacy-sensitive multimodal work

Watch out

Full BF16 needs far more than 24GB — plan on official GGUF / 4-bit packs (and mmproj for vision). Agentic quality ≠ frontier Muse Spark API; validate on your workflows.

Benchmark

DeepSWE 1.1 in context

Only DeepSeek V4-Pro has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

Claude Opus 5 [max]

74%

Rows shown

24

Highest published reasoning effort per model (not best Pass@1)

Snapshot date

2026-08-13

Mirrored from deepswe.datacurve.ai

DeepSWE 1.1 pass@10%16%32%48%64%80%$0$4.50$9.00$13.50$18.00$22.50$27.00Avg cost per taskClaude Opus 5 [max]: 74% · $11.84 · 118k tokens · 99 stepsClaude Opus 5GPT-5.6 Sol [max]: 73% · $8.39 · 60k tokens · 61 stepsGPT-5.6 SolClaude Fable 5 [max]: 70% · $21.63 · 119k tokens · 88 stepsClaude Fable 5GPT-5.6 Terra [max]: 70% · $4.95 · 72k tokens · 76 stepsGPT-5.6 TerraKimi K3 [max]: 69% · $4.65 · 82k tokens · 98 stepsKimi K3GPT-5.6 Luna [max]: 67% · $3.03 · 73k tokens · 102 stepsGPT-5.6 LunaGPT-5.5 [xhigh]: 67% · $7.23 · 46k tokens · 82 stepsGPT-5.5Grok 4.6 [xhigh]: 67% · $5.50 · 71k tokens · 87 stepsGrok 4.6Gemini 3.7 Flash [high]: 65% · $2.18 · 107k tokens · 125 stepsGemini 3.7 FlashDeepSeek V4-Pro [max]: 63% · $0.24 · 106k tokens · 155 stepsDeepSeek V4-ProClaude Opus 4.8 [max]: 59% · $13.22 · 135k tokens · 120 stepsClaude Opus 4.8Qwen3.8-Max [xhigh]: 58% · $3.73 · 95k tokens · 111 stepsQwen3.8-MaxMuse Spark 1.2 [xhigh]: 55% · $3.70 · 99k tokens · 101 stepsMuse Spark 1.2Claude Sonnet 5 [max]: 54% · $26.40 · 214k tokens · 268 stepsClaude Sonnet 5Grok 4.5 [high]: 54% · $2.42 · 36k tokens · 61 stepsGrok 4.5DeepSeek V4-Flash [max]: 53% · $0.10 · 108k tokens · 153 stepsDeepSeek V4-FlashMuse Spark 1.1 [xhigh]: 53% · $2.36 · 74k tokens · 96 stepsMuse Spark 1.1GPT-5.4 [xhigh]: 52% · $5.65 · 71k tokens · 70 stepsGPT-5.4Gemini 3.6 Flash [high]: 47% · $4.42 · 96k tokens · 117 stepsGemini 3.6 FlashGLM 5.2 [max]: 44% · $3.92 · 78k tokens · 129 stepsGLM 5.2Gemini 3.5 Flash [high]: 36% · $3.45 · 76k tokens · 105 stepsGemini 3.5 FlashKimi K2.7 Code: 31% · $2.82 · 59k tokens · 149 stepsKimi K2.7 CodeClaude Sonnet 4.6 [high]: 30% · $5.52 · 76k tokens · 134 stepsClaude Sonnet 4.6Gemini 3.1 Pro [high]: 12% · $2.14 · 28k tokens · 76 stepsGemini 3.1 Pro

Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#ModelPass@1Cost / taskTokens / taskSteps / task
1Claude Opus 5 [max]74%$11.84118k99
2GPT-5.6 Sol [max]73%$8.3960k61
3Claude Fable 5 [max]70%$21.63119k88
4GPT-5.6 Terra [max]70%$4.9572k76
5Kimi K3 [max]69%$4.6582k98
6GPT-5.6 Luna [max]67%$3.0373k102
7GPT-5.5 [xhigh]67%$7.2346k82
8Grok 4.6 [xhigh]67%$5.5071k87
9Gemini 3.7 Flash [high]65%$2.18107k125
10DeepSeek V4-Pro [max]63%$0.24106k155
11Claude Opus 4.8 [max]59%$13.22135k120
12Qwen3.8-Max [xhigh]58%$3.7395k111
13Muse Spark 1.2 [xhigh]55%$3.7099k101
14Claude Sonnet 5 [max]54%$26.40214k268
15Grok 4.5 [high]54%$2.4236k61
16DeepSeek V4-Flash [max]53%$0.10108k153
17Muse Spark 1.1 [xhigh]53%$2.3674k96
18GPT-5.4 [xhigh]52%$5.6571k70
19Gemini 3.6 Flash [high]47%$4.4296k117
20GLM 5.2 [max]44%$3.9278k129
21Gemini 3.5 Flash [high]36%$3.4576k105
22Kimi K2.7 Code31%$2.8259k149
23Claude Sonnet 4.6 [high]30%$5.5276k134
24Gemini 3.1 Pro [high]12%$2.1428k76

DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

Common questions

DeepSeek V4-Pro vs Muse Glimmer 30B

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

Is DeepSeek V4-Pro or Muse Glimmer 30B cheaper for input?

Muse Glimmer 30B is cheaper at $0 per million input tokens, against $0.435 for DeepSeek V4-Pro. 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: API id `deepseek-v4-pro` is unchanged across the 2026-04-24 preview and the 2026-08-13 GA checkpoint. Current list is $0.435/$0.87 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.66/$1.98, peak $1.32/$3.96 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. Muse Glimmer 30B has tiered pricing: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.

Is DeepSeek V4-Pro or Muse Glimmer 30B cheaper for output?

Muse Glimmer 30B is cheaper at $0 per million output tokens, against $0.87 for DeepSeek V4-Pro. 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: API id `deepseek-v4-pro` is unchanged across the 2026-04-24 preview and the 2026-08-13 GA checkpoint. Current list is $0.435/$0.87 per million tokens (cache miss / output) until 2026-08-16 16:00 UTC. From then, official peak/off-peak rates: off-peak $0.66/$1.98, peak $1.32/$3.96 (cache miss / output). Peak hours are 01:00–04:00 and 06:00–10:00 UTC. Muse Glimmer 30B has tiered pricing: Apache 2.0 open weights (BF16 + official GGUF); hosted inference billed by provider.

Which has the larger context window, DeepSeek V4-Pro or Muse Glimmer 30B?

DeepSeek V4-Pro accepts 1M tokens against 131K for Muse Glimmer 30B. This only matters if you routinely send very long documents or large codebases.

Should I use DeepSeek V4-Pro or Muse Glimmer 30B?

Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. DeepSeek V4-Pro suits harder reasoning on a budget; Muse Glimmer 30B suits local agents.

Next step

Choosing between them

Tier and workload decide this more reliably than a leaderboard position does.

If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.

If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.