So according to both:

• https://artificialanalysis.ai/models
• https://artificialanalysis.ai/evaluations/gpqa-diamond

(Which seems to be fairly trustable) - GPT-5.6 Luna (Max) is ranked:

• #10 in "Intelligence"
• #2 in "Speed"
• #1 (Cheapest) in "Cost Per Task" - 5x cheaper than Deepseek V4 (which is already pretty cheap)
• #12 in "Agentic Index"
• #14 in "GPQA (Diamond)" - Above 90% still

From my own personal experience, even on tasks that take up to 5 minutes, I'm only getting a max usage of about 13 AIC per task. Whereas Claude Sonnet 5 could use around 386 AIC for a similar task and similar timeframe.

The code quality seems pretty good too. I really thought it'd be bad for the cost, but honestly I'm super impressed with this model.

It almost reminds me of the premium request days. I even keep refreshing my billing page to see if it's accurate because I'm surprised with the low AIC usage.

Even here https://benchlm.ai/compare:

• It ranks #23, whereas Sonnet 5 ranks #36 (and Luna is about 10x cheaper)

While I'm regular Claude user - and in most benchmarks Opus 5 (max) is usually #1 in all categories - I'm very impressed with OpenAI for Luna and it's ability to compete with even frontier models, at a fraction of the price.

Curious what others think? Or if anyone else has tried it?

Edit: If anyone knows any other & better benchmark sites please let me know as well!

Hey everyone,

Following up on the previous discussion about search API latency, I wanted to share another massive bottleneck we hit while building autonomous web agents. Even with a lightning-fast retrieval API, our agents were frequently getting stuck in execution loops or extracting the wrong data entirely.

We pulled the logs of 10,000 failed retrieval attempts. The culprit wasn't the LLM's logic or the search index itself. The core problem was that LLMs are surprisingly terrible at formulating search queries.

Here are the three main ways agents fail at searching, and the architecture tweak we used to fix it.

1. The Conversational Search Trap

LLMs naturally default to conversational text. Instead of searching a dense keyword string like OpenAI API pricing 2026, a naive agent will search How much does it cost to use the OpenAI API right now?. Search engines (even machine-native ones) often struggle with these conversational strings, burying the agent in SEO spam instead of technical documentation.

2. Context Amnesia in Multi-Hop Searches

When an agent needs to perform a multi-hop search (e.g., finding a founder's name, then finding their previous startups), it frequently drops the subject in step two.

• Search 1: "Who founded Anthropic?" (Result: Dario Amodei)
• Search 2: "What other companies did he work for?" (Result: Completely useless garbage).

3. Ignoring Advanced Operators

By default, agents rarely utilize quotes for exact matches, site: operators to restrict domains, or - exclusions to filter out noise. They rely on basic broad-match strings, which rapidly fills up their context windows with irrelevant tokens.

The Fix: The Query Planner Micro-Agent

We completely stopped letting our primary reasoning agent hit the search tool directly. Instead, we injected a lightweight micro-agent whose sole job is translating the goal into optimized search syntax.

• Step 1: Primary Agent requests information (e.g., "Find Stripe's latest API rate limits").
• Step 2: Query Planner translates this into an array of strict queries: site:[stripe.com/docs](https://stripe.com/docs) "rate limits" 2026.
• Step 3: The system executes the search and returns the clean snippets to the Primary Agent.

This simple architectural tweak increased our agent's successful retrieval rate by over 40% and drastically cut down on wasted API calls.

I posted the full query-planner prompt schema and benchmark logs on the forum if you want to inspect or copy it:Brave vs Google Search API for AI Agents - The 2026 Enterprise Guide

Has anyone else implemented an intermediate query-planning step, or are you successfully prompt-engineering your main agent to handle advanced search syntax on its own?

I got tired of "top 10 AI tools" listicles that never say why you'd pick one

over another, so I built the list I wanted: awesome-open-ai-developer-tools.

Every entry answers three questions — what it does, which proprietary product

it replaces, and why you'd choose it over the closest open-source competitor.

There's a cheat sheet mapping Copilot, Cursor, Devin, Pinecone, LangSmith,

ElevenLabs and others to their open equivalents.

Two things that make it different from the usual link dump:

- Entries carry a "known weakness" line. browser-use is non-deterministic and

hard to debug. Pipecat's own issue tracker documents pipeline freezes.

WebLLM needs WebGPU, so Safari is out. I'd rather you know before you adopt.

- No star counts. They go stale in weeks and turn every PR into a chore.

Available in English, Turkish, Simplified Chinese and Spanish.

https://github.com/Sami-Uysal/awesome-open-ai-developer-tools

Corrections are the most useful thing you can send — wrong license, dead link,

a project I claimed is maintained that isn't. Those get merged fastest.

Hey guys, I recently got GitHub Copilot Student through the Student Developer Pack and I'm a bit confused about the model selection.

Copilot is working fine in VS Code and it shows that my Copilot Student subscription is active. But when I open the model selector, I only get Auto under Copilot.

I can see models like Claude and GPT in the list, but they're greyed out and say Upgrade.

Is Copilot Student supposed to only allow the Auto model, or is there a way to manually select models like Claude Sonnet, Claude Haiku, GPT, etc.?

Just wondering if I'm missing a setting somewhere.

I'm stuck with an annual Copilot plan and it is utterly useless now with the new pricing - i only have access to old and overpriced models. I started just doing BYOK with OpenRouter and Opencode Go and it works fine, but when I go to cancel my copilot plan, they say that I wont be able to use anything at all. Is that just scaremongering and the reality is that i'll only lose the ability to use Copilot's models in Chat, CLI, etc?

I'm trying to use custom endpoints that I have configured in chatLanguageModels.json that I've used for months (and am required to use due to the zero data retention agreement between my org and OpenAI) and it seems something in the latest update has broken them? Photo shows one of the models I set up but there's a whole file of OpenAI models set up that I can't seem to access.

I've tried logging out of GitHub and back in but I keep getting Language model unavailable when I am even able to pick the models I've got configured and half the time when I reload the window, I am not able to pick them at all or even see the settings page for adding models.

At my org we use our personal GitHub accounts and mine is I think a Student account still instead of Free; seems like there's been bugs previously between GitHub accounts and custom endpoints but this is the first time I'm experiencing the bug.

Any assistance would be greatly appreciated.