CodeSOTAIntelligence
Research Memo · ort.fabryka.ai

The OpenRouter LLM Market:
Who Owns Volume vs. Who Owns Revenue

Chinese open-weight labs have captured the majority of token volume on OpenRouter — but a single Western lab still captures the majority of the dollars. The gap between those two facts is the whole investment thesis.

731 days of data (2024-11-09 → 2026-11-09) · measured throughput ~36T tokens/wk · generated 2026-11-09
Thesis — two clocks. Volume and revenue are not rival metrics; they are the leading and lagging indicators of the same migration. Today Anthropic holds ~8% of token volume but ~52% of spend while DeepSeek holds ~38% of volume but only ~11% of spend. The bear case isn't "ignore volume" — it's the opposite: a good-enough model at ~41× lower list price per output token is taking volume inside the same apps that pay for Claude. That is classic low-end disruption, where volume moves first and revenue follows with a lag. The revenue lead is real but is the clock that moves last — so the volume curve is the one to underwrite.
~8% / 52%
Anthropic — volume / spend share
~38% / 11%
DeepSeek — volume / spend share
~36T tokens/wk
measured throughput
731d
daily history captured

1 · Vendor share — the two leaderboards

By token volume (30d)

vendorvolΔ%shareΔpp
deepseek272.2T+75%38.3%+10.8pp
z-ai81.7T+8%11.5%-1.9pp
openai68.0T-10%9.6%-3.8pp
anthropic59.5T+127%8.4%+3.7pp
tencent56.5T+3%8.0%-1.8pp
stepfun35.0T+894%4.9%+4.3pp
xiaomi32.0T-26%4.5%-3.2pp
google24.8T-6%3.5%-1.2pp

By estimated spend (7d)

vendor$Δ%shareΔpp
anthropic$12.6M-1%51.9%-2.9pp
deepseek$2.5M+37%10.5%+2.5pp
openai$2.4M-1%10.0%-0.5pp
google$1.8M+1%7.6%-0.2pp
xiaomi$1.3M-9%5.3%-0.8pp
minimax$799.9K+305%3.3%+2.5pp
z-ai$727.9K-13%3.0%-0.6pp
qwen$629.2K+2%2.6%-0.1pp

Δpp = percentage-point shift in share of the total — the zero-sum view of who's taking ground.

2 · Substitution in the wild — the same apps, both vendors

The clearest evidence that volume migration threatens revenue: the highest-volume apps run both Anthropic and DeepSeek for the same job. 9 of the top 10 apps mix them — and DeepSeek V4 Flash undercuts Claude Sonnet by ~41× on output price. When a workflow already calls both, switching share is a config change, not a migration cost.

appAnthropicDeepSeekmix
Hermes Agent6%51%both
Claude Code46%26%both
Kilo Code1%8%both
pi7%62%both
OpenClaw1%21%both
Codex2%21%both
Cline3%1%both
Portkey AI0%11%
OpenHands1%74%both
ISEKAI ZERO2%22%both

Share of the app's tokens by vendor. Even Anthropic's own Claude Code routes ~13% to DeepSeek; roleplay/agent apps lean majority-DeepSeek. The cheaper model is already inside the funnel.

3 · The substitution engine — families, not versions

Individual versions churn constantly, which makes per-model churn misleading. The real unit is the family: usage migrates within a family (e.g. Claude Opus 4.6 → 4.7 → 4.8) while the family's total tells you whether the franchise is winning. "Lead version" share is a stickiness signal — how fast users consolidate onto the newest release.

familyrecentΔ%lead version (stickiness)migration
DeepSeek DeepSeek Flash (batch252.5T+95%DeepSeek V4.1 Flash 56%vV4 0731→vV4.1 1017
Z.ai GLM Flash (batch)57.1T+3%GLM 5.3 Flash (batch 100%—
Tencent Hy444.0T+5%Hy4 preview 71%v—→v—
OpenAI GPT Luna (batch)43.9T-14%GPT-6 Luna (batch) 75%v5.6→v6
StepFun Step34.2T+961%Step 5 Preview 100%—
Anthropic Claude Opus (batch)32.3T+150%Claude Opus 5.5 (bat 91%v5→v5.5
Xiaomi MiMo Flash24.4T+4%MiMo-V2.6-Flash 100%—
Z.ai GLM (batch)23.4T+22%GLM 5.3 (batch) 83%v5.2→v5.3
nvidia/nemotron ultra 550b a5519.0T-15%nvidia/nemotron-3-ul 100%—
typesafe/jev18.8T+118%typesafe/jev-1.13-20 70%—
OpenAI GPT Sol (batch)16.0T+32%GPT-6.1 Sol (batch) 82%v5.6→v6.1
Anthropic Claude Sonnet (batch14.9T+56%Claude Sonnet 5.5 (b 73%v5→v5.5
Worked example — Claude Opus: the family grew while usage rotated off 4.6 (declining) onto 4.7 (surging) with 4.8 ramping behind it. Healthy in-family substitution = retention, not churn.

4 · Lifecycle of the ecosystem

Every tracked model placed on its curve — launch → ramp → peak → decline → death:

28
LAUNCHING
young & climbing
6
GROWING
rising toward peak
10
MATURE
at peak, plateaued
166
DECLINING
past peak, falling
48
DEAD
retired / substituted

A long "declining" tail is normal — it's last-generation versions bleeding into their successors. "Dead" = had real volume, now ~zero with no in-family heir absorbing it.

5 · Winners & losers (family churn, 30d)

Gaining

  • StepFun Step +961%
  • Anthropic Claude Opus (bat +150%
  • typesafe/jev +118%
  • DeepSeek DeepSeek Flash (b +95%
  • Anthropic Claude Sonnet (b +56%

Losing

  • nvidia/nemotron ultra 550b -15%
  • OpenAI GPT Luna (batch) -14%
  • Z.ai GLM Flash (batch) +3%
  • Xiaomi MiMo Flash +4%
  • Tencent Hy4 +5%

6 · The model treadmill — and China churns fastest

Models don't age gracefully; they get cannibalised by their own successors. Measuring each generation's useful life (launch → usage falling below half its peak) exposes how fast the treadmill runs — and it runs much faster in the East. Average useful life: China ~63 days vs West ~87 days.

generationpeak shareuseful lifeorigin
Deepseek 3.15.9%10dChina
Qwen 2.513.0%11dChina
Deepseek 2.55.8%14dChina
Deepseek 39.4%18dChina
Google 3.73.5%22dWest
Minimax 2.526.2%26dChina
Google 3.51.8%28dWest
Google 3.63.6%30dWest
Minimax 24.5%38dChina
Google 3.82.1%44dWest
Anthropic 4.79.2%46dWest
Minimax 310.7%49dChina

Shortest-lived first. The fastest Chinese generations turn over in under two weeks, while Western flagships hold for two to four months — the table shows the full spread. On average a Chinese generation's useful life is ~63d vs ~87d in the West. Faster churn = faster iteration, but a brutal amortisation window on training spend.

7 · Implications