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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Muse Glimmer 30BMetaRemove
  2. Grok 4.7SpaceXAIRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
muse-glimmer-30b vs grok-4.7 vs deepseek-v4.1-flash
AttributeMuse Glimmer 30Bmuse-glimmer-30bGrok 4.7grok-4.7DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0.35 / 1M$1.60 / 1M$0.30 / 1M
Output$1.50 / 1M$4.80 / 1M$1.20 / 1M
Cache Write (5m)$0.35 / 1M$1.60 / 1M$0.30 / 1M
Cache Write (1h)$0.35 / 1M$1.60 / 1M$0.30 / 1M
Cache Read$0.35 / 1M$1.60 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context131K500K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaSpaceXAIDeepSeek
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryMuse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It combines strong multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across 100+ languages. Designed for long-horizon agentic and coding workflows, Muse Glimmer 30B offers a practical balance of capability and deployment efficiency, making it well suited for local coding assistants, multimodal agents, and production workflows that require sustained autonomous execution.Grok 4.7 is SpaceXAI's flagship model for coding, agentic workflows, and professional knowledge work, succeeding Grok 4.6. It is particularly strong at long-running software engineering, self-verification, and long-context execution, while improving capabilities in document drafting, presentations, and other professional tasks. Trained with extended reinforcement learning focused on multi-hour problems, Grok 4.7 is optimized for sustained, complex task execution and natively supports the Grok Bot harness for conversational workflows. It also introduces an enhanced safeguard stack designed to combine strong jailbreak resistance with low refusal rates for legitimate technical work. Reported benchmark results use xhigh reasoning effort.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.