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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. Gemini 4 ArgonGoogleRemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
  3. Muse Glimmer 30BMetaRemove
gemini-4-argon vs deepseek-v4.1-flash-1017 vs muse-glimmer-30b
AttributeGemini 4 Argongemini-4-argonDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Muse Glimmer 30Bmuse-glimmer-30b
Pricing
Input$2.00 / 1M$0.10 / 1M$0.35 / 1M
Output$10.00 / 1M$0.50 / 1M$1.50 / 1M
Cache Write (5m)$2.00 / 1M$0.10 / 1M$0.35 / 1M
Cache Write (1h)$2.00 / 1M$0.10 / 1M$0.35 / 1M
Cache Read$2.00 / 1M$0.10 / 1M$0.35 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M131K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleDeepSeekMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.Muse 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.