On Oct. 7, Anthropic launched Claude Haiku 5.5. This is a Agent-focused lightweight model for repetitive micro-tasks such as retrieval, classification, and database queries. On the core Agent skill of Computer Use, the task completion rate surged from 16% to 72% vs. Haiku 4.5.
Beyond that, baseline capabilities in knowledge work and coding improved materially. On GDPval-AA, scores nearly doubled and are now close to Sonnet 5.5, Anthropic's mid-sized model.
Versus its closest peer GPT‑6 Luna, Haiku 5.5 leads on overall performance at a similar price point. In knowledge work (GDPval-AA), it is already approaching Sonnet, the mid-sized model in Anthropic's lineup.
The headline remains pricing: Haiku 5.5 cuts input/output token rates by 90% vs. the prior gen, driving a 75% drop in per-task costs. This aligns with the recent trend of aggressive price cuts among closed-source models in H2.
With GPT‑6 Luna first and now Haiku 5.5, the investment cases for Zhipu and MiniMax are being questioned, triggering sharp stock moves. MiniMax, which positions on value-for-money, plunged 12% today.
Let’s dig into Haiku's value-for-money. Key points below:
(1) Performance: broadly on par overall, with different strengths in the details. On composite intelligence, Haiku 5.5 roughly matches the 'Flash' versions of China’s leading models. In sub-use cases, Haiku excels at Agent coding (Terminal‑Bench 4.0) and HLE (Human Level Exams), but trails Chinese models on SaaS workflow automation.
(2) Pricing: Haiku adds real pressure. In the sub‑100k token per‑request tier (which Anthropic says covers ~90% of use cases; beyond that, pricing is 5x higher, erasing the value edge), unit token rates are lower across input, output, and cache write/read.Except for Mimo‑V2.6 Flash which is absolutely cheaper than Haiku, other China 'Flash' models are not cheaper, and MiMo Flash underperforms Haiku. On a per‑task cost basis, Haiku also offers strong value.
Haiku’s launch raises two debates:
1) Closed-source price cuts vs. rising compute costs? Key signals follow.
Dolphin Research believes base compute is indeed getting pricier. First, the following indicators stand out.
Ornn’s B200 rental index rose ~20% over the past 30 days. Agent workloads lift the CPU/GPU ratio, adding CPU cost; CPU vendors such as Intel are raising prices amid tight capacity. DRAM prices are still rising even as the second derivative slows (TrendForce expects Q4 DRAM contract prices to climb 10%–15% QoQ, and including HBM to 15%–20%).
However, lower prices from closed-source models are not purely about weak demand or worsening competition. Lightweight models and architecture optimizations improve GPU efficiency, so even as hourly GPU rents rise, hourly throughput climbs as well, cushioning margins while cutting price.
In addition, portfolio strategy matters. As the lightweight line in the Claude family, Haiku helps with user acquisition and boosts interaction frequency. Sacrificing some Haiku margin—or even taking losses—to replace user retention costs can make sense.
2) North America’s closed-source cuts vs. China’s open-source hikes? The contrast is notable.
Haiku 5.5’s strong value-for-money cannot be ignored for Chinese models. For the first time, a North American model beats most China peers of similar performance on value—for lightweight tasks. At the flagship tier, though, Haiku 5.5 does not change the fact that some Chinese models still hold a value edge.
China’s AI labs are currently prioritizing next‑gen flagships with far larger parameter counts. Leading players are targeting models above the 3T‑parameter level.
We believe the big‑parameter path is right for Chinese models: Scaling laws have not fully broken, and price wars alone are not sustainable. As OpenAI and Anthropic squeeze the share once won by lightweight, value‑oriented models via portfolio tactics, Chinese players must raise flagship competitiveness to retain users on overall cost advantages.
Inevitably, this pushes inference costs higher. Hence China’s end‑user price hikes are largely pass‑through rather than a sign of stronger product competitiveness, though relative value may still remain after the hike.
Valuation takeaways: Based on the last earnings call and recent checks. Updates as follows.
(1) $Z.AI(02513.HK) guides to YE2026 ARR of $3bn (raised from $2.4bn in mid‑Sep). Top‑tier institutions are at ~$3.3bn; after today’s 8% drop, market cap is ~$40bn, taking P/ARR down to 12x.
(2) $MINIMAX-W(00100.HK) still guides to YE2026 ARR of $1bn, unchanged from early‑year; the Street is generally at ~$1.2bn. After a 12% drop today, market cap is ~$9.5bn, putting P/ARR below 8x.
For comparison with the two North American leaders: multiples below. Context matters by stage and model mix.
Anthropic trades around 9x–20x P/ARR, based on ~0.97tn latest May financing valuation to nearly 2tn IPO valuation against ~100bn ARR. The range reflects scenario‑based ARR assumptions.
OpenAI officially guided 2024 revenue of 30bn; third‑party estimates put current ARR at 50bn–70bn, assuming 60bn–80bn by year‑end. With late‑Sep financing valuation of ~1.4tn, P/ARR is also around 20x.
On this view, Zhipu and MiniMax look meaningfully undervalued (even below other unlisted China model names such as Kimi and MiniMax in primary markets). Beyond structurally lower GPM vs. O&A (30% vs. 60%), Dolphin Research thinks the secondary market—especially HK—liquidity and trading conditions are a bigger swing factor.
Dolphin Research will keep tracking US‑China model progress. Near term, we will watch token share on OpenRouter (China has led since Jun): after Haiku 5.5 and Luna, will China’s share be overtaken in Oct–Nov? We will also revisit model economics post Anthropic’s Nov IPO.
