AIHubMix Blog

Announcements, tutorials, model analysis, and product updates from AIHubMix.

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  DeepSeek V4.1 Flash 1017 vs V4 Pro: Is the Flash Enough Now?

  DeepSeek's lineup has an odd shape this autumn. V4 Pro 0813 is the bigger, pricier model: at peak hours its output costs $3.96 per 1M tokens, 3.3 times the Flash's $1.20. But the Flash has been retrained twice since Pro's August release, and the October 17 update, V4.1 Flash 1017, now outscores Pro on every agentic and coding benchmark DeepSeek publishes for both. So is there any coding or agent task left where Pro is worth the premium? By DeepSeek's own numbers, very few. Pro keeps a narrow l

  October 19, 20266 min readOpinion
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  DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5: Which Cheap Model for Terminal Coding Agents?

  Ten days apart, the two cheapest serious agent models of October 2026 arrived from opposite directions. Anthropic shipped Claude Haiku 5.5 on October 7 at $0.10 input and $0.50 output per 1M tokens, pitched as the subagent that sits under Sonnet and Opus. DeepSeek answered on October 17 with V4.1 Flash 1017, an update aimed squarely at end-to-end coding work. The decision comes down to one task: a terminal coding session in Claude Code, where the agent reads files, runs tests, and edits code o

  October 19, 20267 min readOpinion
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  Migrating to DeepSeek V4.1 Flash 1017: The reasoning_content 400, the Silent Alias Swap, and Peak-Hour Billing

  Most migrations start with changing a model name. DeepSeek V4.1 Flash 1017 did it the other way round: on October 17, 2026, DeepSeek kept the name deepseek-flash and changed the model behind it. Anyone calling deepseek-flash, deepseek-v4-flash, or deepseek-v4-flash-vision-exp on DeepSeek's API was moved over without touching a line of code. On AIHubMix the new checkpoint has its own ID, deepseek-v4.1-flash-1017, so you choose when to move. Either way, these are the problems to check: Prob

  October 19, 20269 min readTutorial
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  DeepSeek V4.1 Flash 1017: Same Price, Shorter Outputs, and AIHubMix vs OpenRouter Pricing

  On Saturday, October 17, 2026, DeepSeek swapped the model behind its deepseek-flash API name for a new checkpoint, DeepSeek-V4.1-Flash-1017. The price did not move, the context window did not move, and the calling method did not move. What changed is the model: DeepSeek reports double-digit gains on terminal and program-building benchmarks, and says the new checkpoint finishes tasks faster with shorter outputs. The short answer: if you run coding agents or tool-heavy workflows on the September

  October 19, 202610 min readNews
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  Qwen3.8 Max Free vs Qwen3.8 Flash Free: Is Flash Enough?

  Update (October 27, 2026): The free campaign has ended. GMI Cloud's campaign credits ran out before the planned end around November 13, and qwen3.8-max-free and qwen3.8-flash-free now return 404 model_retired. To keep using Qwen3.8, drop -free from the model ID and call the paid qwen3.8-max or qwen3.8-flash. For most everyday work, yes. Chat, drafting, extraction, summaries and in-editor coding help all run fine on Qwen3.8 Flash, which answers in seconds and, on Qwen's own numbers, sits within

  October 16, 20266 min readOpinion
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  Free Qwen3.8 Max and Flash API on AIHubMix: Limits, End Date, and Your First Call

  Update (October 27, 2026): The free campaign has ended. GMI Cloud's campaign credits ran out before the planned end around November 13, and qwen3.8-max-free and qwen3.8-flash-free now return 404 model_retired. To keep using Qwen3.8, drop -free from the model ID and call the paid qwen3.8-max or qwen3.8-flash. Alibaba's flagship Qwen3.8 Max and its fast sibling Qwen3.8 Flash are free to call on AIHubMix right now. The free model IDs are qwen3.8-max-free and qwen3.8-flash-free, and both cost $0 p

  October 16, 20266 min readNews
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  Qwen3.8 Free on AIHubMix: Fix 429s, "Rate Limited by Provider", and a Drained Token Pool

  Update (October 27, 2026): The free campaign has ended. GMI Cloud's campaign credits ran out before the planned end around November 13, and qwen3.8-max-free and qwen3.8-flash-free now return 404 model_retired. To keep using Qwen3.8, drop -free from the model ID and call the paid qwen3.8-max or qwen3.8-flash. Most problems with qwen3.8-max-free and qwen3.8-flash-free come from three places: the free-tier caps, the campaign capacity GMI Cloud has set aside upstream, and how quickly thinking toke

  October 16, 202614 min readTutorial
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  x402 Tutorial: Let Your AI Agent Top Up AIHubMix with USDC

  AIHubMix now accepts USDC top-ups. Your AI agent can add credit to its own account with the USDC in its wallet: no web page to open, and no one has to click a button. With x402, a top-up can be as small as $1. The agent tops up as it goes, so you don't have to load a large balance in advance. It takes two steps: get a token, a wallet, and some USDC ready on the web and in an app, then run our ready-made top-up script with one command. About 20 seconds after payment, the credit shows up in yo

  October 15, 20267 min readTutorial
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  Moving to Gemini 4 Argon from Gemini 3.1 Pro or 3.8 Flash: What Breaks and How to Fix It

  Gemini 4 Argon became generally available on October 13, 2026, under the model ID gemini-4-argon, on the paid Gemini API, on Vertex AI and on AIHubMix. AIHubMix lists it at $2 per million input tokens, $10 per million output tokens and $0.10 per million cached input tokens, the same as Google's introductory rate. Google's rate runs until November 30; from December 1 it doubles to $4 and $20. Changing the model string is the easy part. Google's What's new in Gemini 4 Argon page ends with a migr

  October 14, 20269 min readTutorial
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  Gemini 4 Argon Thinking Levels and Long Decode: Defaults, Costs and When to Use Each

  Gemini 4 Argon became generally available on October 13, 2026, and with it Google documented the two settings that decide what an Argon request costs and how long it takes: the thinking level and the output limit. Argon offers three thinking levels, low, medium and high, and defaults to medium. Responses stop at 64K tokens unless you turn on long decode, which lets a single response run to 1M tokens. There's a catch in the defaults. Every published Argon benchmark ran at high: Google evaluated

  October 14, 20268 min readTutorial