China vs US LLMs in 2026: pricing, capability, and context window compared

This site tracks 228 US models and 128 Chinese models. On one table a clear trend appears: China’s top tier (GLM 5.2, Qwen3.7 Max) now rivals GPT-5.4 on the Intelligence Index at roughly half the input price and a quarter of the output price, sometimes with a higher coding score. We break down pricing, capability, and context with real data — and how to weigh compliance and latency.

1. The landscape: 228 US models vs 128 Chinese models on one site

As of June 2026, this site tracks 408 active LLM API endpoints across all providers. The split by origin is striking: 228 models from US-headquartered labs (Anthropic, OpenAI, Google, Meta, Mistral, and dozens of smaller outfits), 128 from Chinese labs (DeepSeek, Alibaba Qwen, MiniMax, Moonshot AI, Z.ai, Baidu, and others), 25 from France, 5 from Israel, 5 from Canada, and a scattering from elsewhere. Two nations account for nearly nine out of ten active model endpoints.

A year ago the US–China split was discussed as a frontier-model gap: US labs had the capabilities, Chinese labs had the price. That framing is now outdated. The interesting question in mid-2026 is not "are Chinese models good enough?" for most tasks they clearly are. The interesting question is how to think about the trade-offs when they are not just cheaper but sometimes better on specific metrics, and what operational and regulatory considerations belong in that decision.

This article uses the Artificial Analysis (AA) Intelligence Index — a composite benchmark scored 0–100, where the current maximum achieved is around 60 — and the AA Coding Index as capability proxies, alongside the daily-updated pricing on this site. Both indices correlate well with real task performance on reasoning and software engineering work respectively. They are not perfect proxies for every use case, but they are the best objective, cross-model comparisons currently available.

2. Top-tier head to head

The table below places the strongest US and Chinese frontier models side by side. Prices are USD per 1M tokens (input / output). AA = Artificial Analysis Intelligence Index. Cod = AA Coding Index. Context is the maximum context window in tokens.

A few things stand out immediately. First, the absolute frontier is still US-held: Claude Fable 5 at AA 59.9 and GPT-5.5 at AA 54.8 have no Chinese model within striking distance. If you need the highest possible capability ceiling, the answer is currently US. Second, the mid-frontier band — AA 44–52 — is genuinely contested. Third, the price bands are wildly asymmetric in China's favour.

3. The price gap: similar intelligence, a fraction of the cost

The most striking single comparison in the table is GLM 5.2 vs GPT-5.4. These two models sit at essentially the same intelligence score — AA 51.1 vs AA 51.4, a difference that is within benchmark noise. Yet the price gap is substantial: GLM 5.2 is priced at $1.20 input / $4.20 output versus GPT-5.4 at $2.50 input / $15.00 output.

That works out to GLM 5.2 costing roughly half the input price of GPT-5.4 — but the output side is the starker comparison. GPT-5.4's output rate is $15.00 per million tokens; GLM 5.2's is $4.20. For a workload where output tokens dominate (long-form generation, code synthesis, agentic workflows with verbose tool responses), you are paying about 3.6× more per million output tokens for what the benchmarks say is the same intelligence. At 100M output tokens per month, that is a monthly bill difference of roughly $1,080 — not noise.

The pattern holds across the tier. Qwen3.7 Max (AA 46.0, $1.25/$3.75) sits above Gemini 3.5 Flash on the Intelligence Index while costing 17% less on input and 58% less on output. MiniMax M3 (AA 44.4, $0.30/$1.20) competes in the same AA bracket as DeepSeek V4 Pro (AA 44.3, $0.435/$0.87) — at slightly lower input cost but higher output cost — while delivering 7.5× cheaper output than Gemini 3.5 Flash. DeepSeek V4 Flash at AA 40.3 and $0.09/$0.18 is essentially the cheapest serious model anywhere.

What drives this? Chinese labs face a fundamentally different cost structure. Inference compute costs in China are lower due to domestic GPU supply chains and energy costs. More importantly, the leading Chinese models — DeepSeek's Mixture-of-Experts architecture, Qwen3.7's hybrid reasoning design — were engineered from the start for inference efficiency in ways that the earlier US frontier models were not. DeepSeek V4's MoE approach activates only a subset of parameters per forward pass, cutting the per-token compute cost substantially.

This is a structural advantage, not a temporary promotional price. Expect the price gap in the AA 40–52 band to persist for the foreseeable future.

4. Coding and agentic ability — who actually wins

The AA Coding Index tells a more interesting story than the general Intelligence Index. On coding specifically, GLM 5.2 scores 68.8 — higher than GPT-5.4's 57.2 and Gemini 3.1 Pro Preview's nominally equal 68.8 (though at $2.00/$12.00, nearly double GLM's cost). Claude Fable 5 leads the field at 76.5 and GPT-5.5 follows at 74.9, but both cost significantly more.

For software engineering work in particular — code generation, review, refactoring, test writing — GLM 5.2 is the most cost-efficient model that clears a coding score above 65, and it does so while maintaining 1M-token context. That combination matters for agentic coding workflows: a long context lets the model hold the entire repository structure in its window, and a strong coding index means the per-call output quality is high. Running a two-hour agentic coding session at GLM 5.2 rates ($1.20 input, $4.20 output) rather than at Claude Opus 4.8 rates ($5.00/$25.00) saves on both the large context reads and the verbose code outputs.

To be specific: a coding agent session consuming 500K input tokens and 200K output tokens costs $0.60 + $0.84 = $1.44 on GLM 5.2, versus $2.50 + $5.00 = $7.50 on Claude Opus 4.8. The same session on GPT-5.4 runs $1.25 + $3.00 = $4.25. GLM 5.2 is 5× cheaper than Opus 4.8 and 3× cheaper than GPT-5.4 on this workload — while delivering a higher coding benchmark score than both.

The honest caveat: benchmark scores do not capture everything. Claude Opus 4.8 and GPT-5.5 have demonstrated stronger agentic reliability in real-world long-horizon tasks — following complex multi-step instructions, recovering gracefully from unexpected intermediate results, and maintaining coherence over very long agent loops. Those qualities are hard to capture in a static benchmark and are worth paying for on genuinely complex, hours-long tasks. For shorter, more structured coding tasks, GLM 5.2's benchmark advantage is likely to translate to real performance.

MoonshotAI Kimi K2.6 deserves a specific mention for agentic use. Despite a lower AA score (42.8), it was designed explicitly for tool-use and multi-step agent patterns, and practitioners report it outperforming its benchmark score on structured agentic pipelines. Its 262K context is smaller than the 1M context of the other Chinese flagships, which constrains very large-repo work, but for bounded agent tasks it is competitive and cheap ($0.67/$3.50).

5. Context windows: parity at the top

One narrative from 2024 was that US models held a meaningful lead in context length. That story is largely over. The Chinese top tier — GLM 5.2, Qwen3.7 Max, MiniMax M3, DeepSeek V4 (both variants) — all offer 1M-token context windows. That matches the 1.05M context offered by GPT-5.x and Gemini 3.x, and is well within the range needed for whole-repository code tasks, long-document analysis, and multi-hour agentic runs.

At the ultra-long end, the US still has standout offerings: Llama 4 Scout supports 10M tokens, and Grok 4.x reaches 2M. Those are primarily relevant for niche document-processing workloads where the entire corpus fits in a single prompt — academic literature review, legal document sets, codebase-wide analysis — rather than typical production agent tasks. For the 99% of production workloads that fit comfortably within 1M tokens, context length no longer differentiates US and Chinese flagship models.

The Kimi K2.6 exception (262K) matters less than it initially appears: Moonshot built it for agentic structured tasks where context is managed via tool calls and retrieval, not raw window size. In practice, its 262K is rarely the binding constraint on the use cases it is designed for.

What does still differ is context pricing. Long-context tasks at US flagship rates can get expensive fast. At 1M tokens of input per call, Claude Opus 4.8 at $5.00/M costs $5.00 per call on input alone. GLM 5.2 at $1.20/M costs $1.20. For a workflow running 50 such calls per day, the monthly input cost is $7,500 versus $1,800 — a $5,700 monthly difference for the same context length capability.

6. How to choose: compliance, latency, data residency

The pricing and benchmark comparison above points toward Chinese models for a wide range of cost- sensitive workloads. But there are real considerations that the numbers alone do not capture, and engineering decisions made purely on benchmark cost ratios without accounting for them will cause problems in production.

Data residency and compliance. If your application handles data subject to GDPR, HIPAA, CCPA, SOC 2, or sector-specific financial or healthcare regulations, the question is not just where the API call goes but where inference is executed and whether logs are retained. US providers (Anthropic, OpenAI, Google) offer data processing agreements (DPAs), BAAs for HIPAA, EU region hosting, and enterprise compliance certifications that Chinese providers have not yet achieved at the same level for non-Chinese customers. For regulated workloads in the US and EU, this can be a hard blocker regardless of price.

Regional latency. API latency from US-based infrastructure to GLM 5.2, DeepSeek, or Kimi endpoints routed through Chinese data centers is meaningfully higher than to US-based providers — typically 200–400 ms added round-trip for direct API calls, depending on routing. OpenRouter and other aggregators mitigate some of this by using edge caching and regional proxies, but for interactive, latency-sensitive applications (real-time chat, voice-assistant backends, sub-100ms tooling), this latency floor can disqualify Chinese providers regardless of cost. For async workloads (batch jobs, nightly analysis, background agents), latency rarely matters.

Provider reliability and SLA. Chinese frontier model providers are scaling infrastructure rapidly, and reliability track records for Western customers are shorter than those of OpenAI or Anthropic, which have been serving enterprise customers at scale for years. This does not mean they are unreliable — DeepSeek and MiniMax have both maintained solid uptime through OpenRouter — but it does mean the risk profile is different for SLA-sensitive production systems. Consider running A/B traffic between a Chinese primary and a US fallback if reliability is a hard requirement.

The practical decision tree. Start with the compliance filter: if your data is regulated under a framework that requires US or EU data residency and processing agreements, use US providers and stop. If latency is a hard constraint below ~300ms round-trip from US infrastructure, use US providers. If neither applies — batch workloads, internal tooling, development environments, non-regulated applications — then the benchmarks and pricing above make a compelling case for GLM 5.2 at the AA 50 tier, and DeepSeek V4 Pro or MiniMax M3 at the AA 44 tier. For absolute frontier work where benchmark ceiling matters, Claude Fable 5 or GPT-5.5 remain the only options.

The simplest summary: best-value rankings on this site sort first by quality floor, then by intelligence per dollar. Chinese models dominate the value tiers. US models dominate the absolute frontier. Most workloads live in the value tiers. Run the cost calculator on your actual token volumes to make the comparison concrete.

Written by Allen Pan. Corrections or questions welcome — allen@xyzsleep.com.