OpenRouter

Stripe acquired model routing platform OpenRouter for $7B. In this episode, co-founder Alex Atallah and investor Anjney Midha trace its path from being dismissed by VCs as 'just a wrapper' to handling over 10 trillion tokens per day. They discuss why model labs spend billions on training yet fail at distribution, how Mistral's price war proved the inference marketplace, and OpenRouter's early failed model fusion experiments that were revived years later. Anjney also explains why Stripe's fraud infrastructure matters strategically for OpenRouter and warns that the next wave of fraud will come from autonomous agents attacking token flows.

Moonshot AI released Kimi K3 weights on Hugging Face under a custom license that isn't OSI-approved, so it's open-weight, not open-source. The checkpoint is a 2.8T-parameter MoE with 104B active parameters per token, stored in MXFP4. The license allows commercial use, modification, and distribution, but adds two conditions: if you run a Model-as-a-Service business with over $20M annual revenue, you need a separate agreement with Moonshot; if your product exceeds 100M MAU or $20M monthly revenue, you must display 'Kimi K3' on the UI. Internal use and access via official partners are exempt. On OpenRouter the model ID is moonshotai/kimi-k3, accepting text, image, and video input with a 1,048,576-token context window. No free tier.

OpenRouter just listed two new OpenAI models: GPT-6 Sol and GPT-6 Luna. Pricing is half that of the previous GPT-5.6—Sol at $2/M input and $10/M output, Luna at $0.10/M input and $0.50/M output. On AutomationBench, both beat the prior best score while costing less per task. The post doesn't disclose exact scores or latency figures.

Anthropic released Claude Opus 5.5 on OpenRouter, the first model in the Claude 5.5 series. It beats Opus 5 and Fable 5.1 on agentic coding, knowledge work, and computer use, with a 1M context window. Pricing is $4 per million input tokens and $20 per million output tokens, 20% cheaper than Opus 5. The post doesn't include benchmark scores or latency figures.

NVIDIA positions Nemotron 3.5 Lightning as the execution layer in agent workflows—handling frequent tool calls, file reads, and result checks rather than heavy planning. It's a 30B MoE model that activates only ~3B parameters per token, keeping latency and cost low for high-volume calls. It complements, not replaces, Nemotron 3 Ultra. Weights are open, with tool calling and structured output support. Context goes up to 1M tokens, though OpenRouter's standard tier caps at 262K. Worth a look if your agent makes many model calls per run.

OpenRouter sent the same prompt to 20 image models and read the actual billed cost. GPT Image 2 was cheapest at $0.006 per 1024×1024 PNG; Gemini 3 Pro Image was priciest at $0.134—a 22x spread. Pricing units differ across providers (tokens, megapixels, per image), so side-by-side list prices mislead; generate once and check usage.cost. Five of six models rendered text correctly, including the cheapest. Recraft V4.1 Vector outputs editable SVG at $0.08. The post also details formats, resolution caps, and seed support per model.

The author paid Gemini 3.1 Pro $9 to label 4,290 Reddit comments with knife brands, models, and steels, then fine-tuned GLiNER large v2.5 on those labels. The resulting model runs locally and hits 0.83 F1 against Gemini's labels after 24 minutes on a Tesla T4. Zero-shot GLiNER scored roughly 0.65 F1. The hardest bug was words_mask: the docs suggest a binary mask, but it's actually a word index; filling it with ones kept loss flat at 70. Five of ten runs produced no usable model—three config failures, two from words_mask. The post doesn't report human accuracy on Gemini's labels, so 0.83 is measured against Gemini, not ground truth.

CrofAI marketed itself as the world's cheapest inference provider but was caught by developer Kendell silently routing API calls to smaller, cheaper models on OpenRouter. For example, requests for Kimi K3 at $2/$10 in/out were actually served by GLM 5.3 Flash, a 13–20x markup. The founder also fabricated a 'greg' model family that simply pointed to existing open models like GLM 5.2 and Qwen 3.5 9B. After the exposé, he denied everything, then claimed a 'team' was taking over, and within hours deleted the website, Twitter account, and subreddit. The post also notes his hardware claims don't add up: Kimi K3 needs at least 802GiB of VRAM even at Q2_K quantization, but the largest RTX Pro 6000 machine on Vast only offers 765GiB.

OpenRouter Fusion is a compound inference pipeline, not a new model. It fans out one prompt to up to 8 panelist models in parallel, has a judge compare their answers for consensus and blind spots, then lets the calling model write a final synthesis. A default three-model panel costs roughly 4–5× a single completion and takes 2–3× longer. On the DRACO deep-research benchmark, a budget panel of Gemini 3 Flash, Kimi K2.6, and DeepSeek V4 Pro scored ~64.7%, close to Claude Fable 5’s solo 65.3%. OpenRouter’s own test paired two Claude Opus 4.8 runs and saw a 6.7-point gain over a single run. The team positions Fusion as an escalation path for complex research and high-stakes decisions, not for simple chat. The post does not disclose per-token pricing, only the cost multiplier.

OpenRouter published a hands-on review of Seedance 2.5 on Sep 9. The model went live Aug 7, 2026, and excels at 30-second single takes and editing from existing footage. Cost is ~$0.103/sec at 480p and ~$0.231/sec at 720p; using a video reference cuts the token price by ~40%. Audio generation adds no extra charge. The clear trade-off: it caps at 720p. For 1080p or 4K, you still need Seedance 2.0 or Veo 3.1. Frame-exact reproducibility is also not guaranteed.

OpenRouter added a server-side shell tool and Files API so any model can run commands inside a hosted Linux container. Sandbox time costs $0.0001 per second, billed with the request. Network is off by default; you can enable it with an allowlist. The Files API handles uploading inputs and downloading outputs. The shell tool supports both OpenAI and Anthropic tool specs—set engine: openrouter to force server-side execution. The post doesn't disclose container resource limits or max runtime per invocation.

OpenAI's new flagship GPT-6 Astra is now listed on OpenRouter, released Sep 4, 2026. It's positioned for demanding end-to-end work: advanced analysis, software engineering, deep research, science, and document creation, with a stated strength in long-horizon agentic tasks involving computer and browser use. Pricing is $10/$50 per 1M tokens, 1M context window. The fastest provider on OpenRouter is OpenAI Fast at 2.10s latency but $20/$100; the best value is OpenAI Flex at $5/$25 with 2.72s latency and 56 tps throughput. The post does not disclose benchmark scores or comparisons to other models.

OpenRouter data shows agents consume 7.3T tokens weekly, nominally 5.2× human usage. But 70–85% are cached reads; with ~90% discount, the real bill is roughly 2×. GitHub's Knowledge Compressor prototype halves doc length and claims breakeven at 2,000 reuses, but factoring in caching pushes the median to 5,000+. OpenAI's Jalapeño chip beats Nvidia GB200/GB300 on fixed-length benchmarks, yet lacks AgentX scores for real agent workloads. All three stories share one distortion: prompt caching inflates headline numbers.

Anthropic released Claude Fable 5.1 on OpenRouter as a direct upgrade to Fable 5. The focus areas are agentic coding, long-running workflows, visual code generation, finance, and analytics. The post doesn't disclose benchmark numbers or pricing changes, so I'd wait for third-party evals.

Tang Jie posted that GLM-5.3 Flash AA (codename Ox Alpha) scored 57 on OpenRouter at 1/100th the price of frontier models. It runs entirely on domestic Chinese chips and captured nearly 20% of weekly token share, ranking first. The post doesn't disclose the chip model, benchmark details, or comparison targets.

Z.ai confirmed it is the lab behind Ox Alpha, the open-weight model that appeared anonymously on OpenRouter and immediately topped rankings. The company calls it the newest GLM iteration, built for coding, sustained agentic work, and multimodal reasoning. Weights drop Wednesday for developers to build on. Earlier GLM-5.3 already matched Anthropic's Fable 5 on some benchmarks. Ox Alpha adds more pressure on frontier pricing from OpenAI and Anthropic.

Yage breaks down 'local LLM' into two markets: a cost market buying 5–20× price gaps, and a control market buying 25–33-year certainty. Using a four-quadrant framework (open/closed weights × time/token billing), the piece explains why surging open-weight model usage on OpenRouter doesn't mean local deployment is winning. Self-hosting payback depends entirely on which cloud billing mode you replace—decades for subscriptions, months for high-cache-hit agent API calls. In July–August 2026, Anthropic and others made four moves at the inference layer: silently remapping parameters, repeatedly extending usage boosts, adding watermarks, and redefining self-hosting as 'your harness plus my inference.' But simultaneous deep price cuts mean the misalignment is real but direction is unresolved.

CTGT traced the anonymous model Ox Alpha, which appeared on OpenRouter on Aug 20, to Zhipu's GLM-5.x family via an 11-of-11 tokenizer match and parameter checks. On sensitive topics, Ox Alpha answers like an American model on Xinjiang and Taiwan but flatly refuses 7 domestic-risk topics including Xi Jinping personally—a blacklist-style censorship distinct from DeepSeek's pervasive softening. Three refusals still billed completion tokens; one later returned a full answer on retry.

OpenRouter data shows that during OpenAI's July 27–Aug 14 discount on Terra and Luna, daily Terra tokens rose 5.6x and Luna 13.8x, while the undiscounted Sol model saw only a 1.1x bump. Most of the share gain came from competitors: the OpenAI family's token share grew from 7.1% to 12.4%, with roughly three-quarters taken from outside labs. After the discounts ended, about 32% of the 100K+ users who tried Terra/Luna kept using them, and 18% ran at or above their discount-period pace. Sol later reproduced the same spike when it got its own 50% discount on Aug 17.

A free reasoning model called Ox Alpha appeared on OpenRouter Thursday, described as built for coding and sustained agentic work. Stripe CEO Patrick Collison called it 'very impressive' on X. The listing says it's a 'stealth model' from an anonymous third-party provider. Speculation centers on two theories: an unreleased GLM model from Chinese company Zhipu, or a hidden version of Microsoft's MAI. Reddit and X are split, but the article offers no hard evidence—only community guesses.