GPT-5.6 is live on OpenRouter!
Sol (flagship), Terra (balanced), and Luna (fast + low-cost).
In our early testing, the 5.6 models were more token efficient, leading to lower cost and faster time to task completion. AA's results agree.
More on Sol, Terra, and Luna below 🧵
GPT-5.6 Luna: the fast, low-cost tier. Tuned for high-volume, latency-sensitive work like chat, classification, and lightweight agents, with real GPT-5.6 reasoning.
Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency.
x.ai/news/grok-4-5
Use Grok 4.5 from @SpaceXAI the moment it goes live, with no code change.
~x-ai/grok-latest always routes to the newest Grok. Point at it now (Grok 4.3 today) and it auto-upgrades to 4.5 the second it ships.
Try it:
There are countless useful insights in OpenRouter data, which we make public to the world to improve everyone's use of intelligent models.
DM me if you're obsessed with data and LLMs and want to be a part of something exciting coming up!
We benchmarked 1,730 visual reasoning questions across 5 models to test a common cost-saving trick: setting image detail to "low."
Surprise finding: it often backfires. The model burns extra reasoning tokens squinting at a blurry image, and overall cost increases 👇
We benchmarked 1,730 visual reasoning questions across 5 models to test a common cost-saving trick: setting image detail to "low."
Surprise finding: it often backfires. The model burns extra reasoning tokens squinting at a blurry image, and overall cost increases 👇
So what actually moves the bill? Reasoning effort, by a wide margin.
Detail swung accuracy 2 to 17 points while barely touching cost, while capping reasoning effort cut cost 50 to 75 percent with accuracy moving 1 to 2 points, within noise.
TLDR:
Reasoning models → keep detail on auto or high, control cost with reasoning effort
Non-reasoning models → low detail genuinely saves money and latency if you can absorb the accuracy hit
Full benchmark with per-model tables and methodology:
every model choice is a bet.
it shouldn't be a blind one.
750T+ tokens routed across every major model and provider — what actually moves cost, latency, and reliability.
live at the top of salesforce tower w/
@deedydas (menlo) + @shashankgoyal95 (openrouter)
rsvp 👇
DeepSeek doubled its token share on OpenRouter in six months, going from 9% in January to 18% by June.
The driver is agentic work. New data deep dive in our blog 👇
V4 also broke DeepSeek away from the other Chinese models.
At 6.6T weekly tokens it now runs well ahead of Xiaomi (4.0T), Minimax (3.6T), and Tencent (3.8T), and it edges out Anthropic's 6.1T as the highest-token-volume model lab on the platform.