OpenRouter 上的 AI 模型市场细分：GPT-OSS 120b 流量达 Opus 4.8 的三分之一

In 1989 Boris Yeltsin stopped at a Randalls supermarket in Houston, stunned by the variety of ice cream.¹ OpenRouter is that supermarket aisle for AI.

And shoppers make surprising choices : OpenAI’s year-old open-source model GPT-OSS 120b² commands 36% of Anthropic’s Opus 4.8 volume.³

Why does a model from August 2025 still hold a third of the traffic of a frontier model that shipped weeks ago?

The market for tokens has segmented.

Daily tokens served on OpenRouter, 7-day average: GLM 5.2 tops the field at 495B, above Claude Opus 4.8 at 199.6B; a year-old GPT-OSS-120b holds 71.3B, roughly a third of Opus, with Gemma 4 26B at 50B & the rest of the open field trailing to Phi-4 at 90M

Segmentation happens because buyer needs vary.

The segmentation is accelerating driven by competition. Last week Anthropic shipped Opus 5, smaller & cheaper than Fable,⁴ explicitly to contest the ground that Moonshot’s Kimi 3 targets.⁵ In the mid-model-market, Poolside launched Laguna S 2.1, a US mid-market model.⁶

Size (small, medium, large, XL), origin (US v China), architecture (dense vs sparse), accuracy (coding focused or general), speed (tokens per second), modality (text-only or vision) ; there are many flavors of AI.

I spent the weekend replacing the model that runs my agent. The incumbent is Gemma 4 26b; the challenger is Laguna S 2.1, a 118-billion-parameter model. By every number I expected to matter, the 118b model should have been slower.

On my M5 Max both generate at the same speed, because Laguna is a mixture-of-experts architecture: 118 billion parameters live in memory, but only 8 billion activate per token. A 118b model now runs at the decode cost of a 26b model, which pulls frontier-class quality down into the local tier.

The accuracy shows up where it matters. My local stack runs a coding & email agent on tool calls, & across the models I have cycled through, the tool-call failure rate falls from 29.4% to 20.1% as active parameters climb.⁷ Laguna reduces error rates by 7 percentage points over the 26b model it replaced.

MCP tool-call failure rate for the local model driving a production automation stack, measured from real traffic: Ornith-1.0 35B at 29.4%, Gemma 4 26B at 27.1%, & Laguna S 2.1 at 20.1%, roughly a quarter fewer failures than Gemma

Segmentation is the sign of a healthy competitive market. The frontier still serves the world’s hardest tokens. It no longer has to serve all of them, & the tier on my laptop just got a much higher ceiling ; a trend that competition will push forward inexorably.

Boris Yeltsin’s 1989 visit to a Houston grocery store, a Randalls in Clear Lake, September 16, 1989. ↩︎
Introducing gpt-oss (OpenAI), released August 5, 2025. ↩︎
OpenRouter model activity pages, 7-day average, retrieved 2026-07-27: GPT-OSS-120b, GLM 5.2, Claude Opus 4.8. ↩︎
Anthropic debuts Claude Opus 5 at half the price, launched July 24, 2026. ↩︎
Moonshot’s Kimi 3 is expected to close the gap with Anthropic’s Opus 4.8 (TechCrunch). ↩︎
Introducing Laguna S 2.1 (Poolside), a 118B-total, 8B-active open-weight model released July 22, 2026. ↩︎
Author’s production data: MCP tool-call logs from a local coding & email agent, measured across three local models over five months. ↩︎