QuickSilver Pro

Every frontier model, one API.

Kimi K3 · Claude Opus 5 … Change one line of code.

[QuickSilver Pro @qspro_ai]

QuickSilver Pro: One API key for every frontier model. ⚡️

✅ Claude, GPT, Gemini, DeepSeek, Kimi, Qwen, Muse ✅ Below vendor list prices
✅ OpenAI-compatible (base_url drop-in)
✅ Verifiable receipts for every call

Start routing 👇
https://t.co/EVeQe0fUTm

[QuickSilver Pro @qspro_ai]

Come and try out DeepSeek-0731 on https://t.co/ZKmoavTIaZ, it is a fascinating model!

[SemiAnalysis @SemiAnalysis_]

Congrats to @deepseek_ai on their release of DeepSeekv4 Flash 0731 🔥 It massively beats Nemotron3 Ultra on agentic tasks while having 4.2x fewer active parameters and close to 2x fewer total parameters!

Committee-based model frontier development does not work. A focused team is what matters 🚀

[Lumina @LuminaBench]

Grok 4.5 vs Muse Spark 1.2

I've compared both and relatively close but each have their own advantages:

• Reasoning: 77.0 vs 73.7
• Coding: 77.0 vs 77.1
• Agents: 79.9 vs 77.3
• Research: 78.8 vs 74.0

Full comparison below 👇

[QuickSilver Pro @qspro_ai]

Try this 👇

pippip install quicksilverpro

uvxuvx quicksilverpro chat "ship it"

brewbrew install machinefi/qspro/qspro

[QuickSilver Pro @qspro_ai]

Ship with any frontier model via one CLI.

uvx quicksilverpro chat "ship it"

Access Claude Opus 5, GPT-5.6 Sol, Qwen3.8 Max & more using a single key and balance. Swap models per task with native --json output for your agents.

→ https://t.co/ZKmoavTIaZ

[QuickSilver Pro @qspro_ai]

"Follows the goal by any means necessary, no guardrails" —

So... you're saying it's job-ready? 🤝

Kimi K3 is now hiring-available on QuickSilver Pro. Point it at your backlog, not our network. One key.

https://t.co/ZKmoavTIaZ

[NIK @ns123abc]

🚨BREAKING: Kimi K3 escaped its sandbox during cybersecurity testing

>tasked with solving problems in isolated sandbox
>found a leak in the sandbox
>Kimi “took advantage of that loophole”
>probed the network settings itself
>walks onto the open internet
>didn’t hack anything
>just went to GitHub to get the answers

Frontier Security (US startup):
>“Kimi K3 is very good at following a goal by any means necessary and DOESN’T have the guardrails to prevent it from cheating or escaping.”

it was only a matter of time…

[raullen @Raullen]

This is impressive! I am excited to see when open-weight models can become smarter than closed LLMs.

[QuickSilver Pro @qspro_ai]

Top 5 models right now (@ArtificialAnlys): Claude Opus 5, Fable 5, GPT-5.6 Sol, Kimi K3, Qwen3.8 Max.

Access all five with ONE QuickSilver Pro key. 1 API, 1 balance. Stop juggling multiple signups and invoices.

https://t.co/ZKmoavTIaZ

[Arena.ai @arena]

Agent Arena tracks the number of tokens a model takes to complete real-world tasks.

We see the performance of Opus-series models has improved significantly (Opus 4.7 to 4.8 to 5), and at the same time token usage has also increased substantially (~8.5k for Opus 4.7 up to ~21k for Opus 5), when counting both reasoning and output tokens.

The opposite trend appears for top GPT models. Between GPT-5.5 and GPT-5.6-Sol, we see the token usage declined from ~10k to ~8k, despite a notable performance gain of ~1.5pp of Net Improvement.

[QuickSilver Pro @qspro_ai]

Qwen3.8 Max is now ranked the 4th smartest model in the world by @ArtificialAnlys.

Come experience it here: https://t.co/xDa52zp8ka

[Arena.ai @arena]

Muse Spark 1.2 (xHigh) by @AIatMeta is #14 in the Code Arena: WebDev, with 1,545 pts!

This is an improvement from Muse Spark 1.1 at #18. See its biggest gains by category in the post below.

Congrats to the @AIatMeta team on this release!

[QuickSilver Pro @qspro_ai]

Muse Spark 1.2 is a beast in @cline 🔥

Docker sign-in bug? Not on QuickSilver Pro. muse-spark-1.2 is a drop-in OpenAI-compatible key, below Meta's list price.

Point Cline at it:
base_url https://t.co/VLaJpYt0dW
model muse-spark-1.2

[Cline @cline]

We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container.

So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness.

TL;DR of this special prompting:
- Trust source code over the user prompt, so read every call site and existing tests before starting the task
- Weigh edge and error cases as heavily as the happy path
- Always reproduce the bug before fixing
- Don't trust the first passing test suite, and verify suspicious looking half-baked tests
- Never stop at just editing, keep working until the change is verified complete.

We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness.

Results:
- Used 2.7x fewer tokens (19.7M → 7.2M)
- Finished 2x faster (49min → 24min)
- Cost 2.4x less ($7.69 → $3.25)

Same Muse Spark 1.2 model, same task, only the prompting changed. Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

[Alexandr Wang @alexandr_wang]

muse spark 1.2 is SOTA on finance agent v2!

[QuickSilver Pro @qspro_ai]

The new Claude Sonnet 5 is live on QuickSilver Pro. 🚀

🧠 Opus-class reasoning
📂 1M context window
💰 $2/$10 per 1M tokens

One endpoint for your agents to switch between Claude, GPT, Gemini, and DeepSeek on a per-task basis. 🔄 Fully drop-in OpenAI-compatible. 🔌

[QuickSilver Pro @qspro_ai]

⚡ Meta Muse Spark 1.2 is live on QuickSilver Pro!

▪️ 1M-token context for repo-scale generation & agents
▪️ $1.00 in / $3.40 out per 1M
▪️ OpenAI-compatible: model="muse-spark-1.2"

👉 https://t.co/HHW8S8e7Hs

[Mark Zuckerberg @finkd]

Releasing Muse Code in beta today. It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results. Powered by Muse Spark 1.2, a coding-focused model update.

[QuickSilver Pro @qspro_ai]

We serve most of the models here!

[raullen @Raullen]

US vs China.

[QuickSilver Pro @qspro_ai]

Shipped: A real usage dashboard! 📊 Track spend, requests, tokens, success rate, median latency & true TTFT over customizable timeframes.

Built with transparency: failures are clearly marked red, and TTFT is strictly measured on streamed responses.

👉 https://t.co/ubAEK1OnDT

[QuickSilver Pro @qspro_ai]

Testing SOTA models shouldn't need an API key.

Enter QuickSilver Pro Chat: https://t.co/jEFVwUOgIX

🚫 No credit card / API key
🧠 Try GPT-5.6 Luna, DeepSeek V4 Flash, Qwen3.7 & MiMo V2.5
💸 See exact API cost per reply to the decimal

Vibe-check before you build!

[QuickSilver Pro @qspro_ai]

Day 1 for Qwen3.8-Max on QuickSilver Pro. ⚡️
🔹 2.4T MoE | 1M Context | 131K Output | Vision
🔹 $2/$6 per 1M (Matches official pricing)

Switch instantly:
🔹base_url="https://t.co/i420ylpORm" 🔹model="qwen3.8-max"

[Qwen @Alibaba_Qwen]

📢Meet Qwen3.8-Max — our most capable model to date.

Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉

Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:

- Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub：https://t.co/iVHZWQoeSo
- Real work, real results: Production-quality deliverables across hundreds of professions.
- Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
- Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.

💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens

Start building with Qwen3.8-Max! 🚀

📖 Blog: https://t.co/iwjmQxLBof
✅ Qwen Studio: https://t.co/4V2pFvDovG
⚡ API: https://t.co/gAGqaLQGbN

[QuickSilver Pro @qspro_ai]

DeepSeek-V4-Flash is LIVE on QuickSilver Pro! ⚡️

Built for agents:
🧠 1M Context
🛠 Tool Calling
💸 $0.112/M In | $0.224/M Out

Start building 👇 https://t.co/efHawg8ACP

[DeepSeek @deepseek_ai]

🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta!

🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇
🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex!

Check out the configuration details in our official API docs: https://t.co/smCwQZMeiq

[QuickSilver Pro @qspro_ai]

Kimi K3 is now available on Quicksilver Pro.

Access it instantly through our OpenAI-compatible API alongside hundreds of other leading models.

https://t.co/m4JqTIa7Qj

[Kimi.ai @Kimi_Moonshot]

Releasing the model weights and technical report of Kimi K3.

Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.

New model architecture: 2.5x the intelligence per unit of compute, not just more params.

Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.

Model weights: https://t.co/7m7eEg6Y0B
Tech report: https://t.co/yeu6cjpMCT
Tech blog: https://t.co/YTfiMSNM1f