The Chinese AI Models That Cost 1/10th the Price (Adjusted for Token Efficiency)

Most teams default to GPT-5, Claude Sonnet, or Gemini Pro. These cost $6–$11 per 1,000 requests at list price. Chinese AI labs have shipped models that match or beat those benchmark scores — and when you adjust for token efficiency, the real savings range from 8x to 57x depending on the swap.

This isn't a handful of niche models. Seven Chinese labs — DeepSeek, Xiaomi, Z AI (Zhipu), Kimi (Moonshot AI), MiniMax, Alibaba (Qwen), and Baidu (ERNIE) — now have models in our database with published pricing and benchmark scores. Eight of those models score 39+ on the Intelligence Index, putting them in the same tier as Claude 4.5 Sonnet and GPT-5.

How we calculate cost comparisons

Raw price-per-token comparisons are misleading. Different models consume different amounts of tokens for the same task — a model that's verbose burns more tokens even if its per-token price is lower.

We normalize token counts using data from the Artificial Analysis Intelligence Index (AAII) benchmark. Every model in their evaluation runs the same set of tasks. If model A uses 200M input tokens to complete the benchmark and model B uses 100M, we estimate model B will use half the input tokens for any equivalent workload. This is the same normalization our cost simulator uses in production.

All pricing data comes from the MarginDash database: 397 models across 41 vendors, synced daily.

The standout Chinese models

These are the eight Chinese models scoring 39+ on the Intelligence Index — the threshold where models start matching Western flagship performance. Sorted by Intelligence Index, highest first.

The top scorer is Z AI's GLM-5 at 49.6 — higher than Claude 4.5 Sonnet (42.9) and GPT-5 medium (41.8). At list price it costs $2.60 per 1,000 requests vs Claude Sonnet's $10.50 and GPT-5 medium's $6.25. But list prices don't tell the full story — different models consume different amounts of tokens for the same task.

Head-to-head: normalized cost comparison

Here's what happens when you normalize for token efficiency using AAII benchmark data. The “Eff. $/1K” column shows what the Chinese model would actually cost for the same workload, adjusted for how many tokens each model consumes to complete the same tasks.

The savings vary significantly by pair. GPT-5 medium to MiMo is 14x after normalization — MiMo uses about 44% more input tokens and 92% more output tokens for the same benchmark tasks, which eats into the raw price advantage but still leaves substantial savings with nearly identical Intelligence Index scores (41.8 vs 41.4).

The Claude 4.5 Sonnet (Reasoning) to DeepSeek V3.2 swap shows the most dramatic result: 57x. This is driven by DeepSeek using 85% fewer input tokens than Claude Sonnet for the same tasks — a significant token efficiency advantage that amplifies the already lower list price.

At the top of the market, Kimi K2.5 matches Claude Opus 4.6 (Non-reasoning) on Intelligence Index (46.7 vs 46.4) at 8x lower normalized cost. MiniMax-M2.1 actually scores higher than Claude 4.5 Sonnet (Non-reasoning) — 39.5 vs 37.1 — at 8x less.

Where Chinese models are strong

The benchmark data shows clear strengths:

• Advanced reasoning. Six of the eight flagship Chinese models are reasoning models. GLM-5 scores 49.6 on the Intelligence Index — higher than every Western model except Claude Opus 4.6 (53.0). Kimi K2.5 scores 46.7, ahead of GPT-5 (high) at 44.6.
• Graduate-level Q&A. Kimi K2.5 hits 88% on GPQA, matching or exceeding most Western flagships. GLM-4.7 and Qwen3 Max both score 86%. These aren't watered-down models — they're competitive on the hardest public benchmarks.
• Normalized cost efficiency. After adjusting for token efficiency, DeepSeek V3.2 and MiMo-V2-Flash deliver 12–14x savings over GPT-5 medium with comparable Intelligence Index scores. GPT-5 mini (II 41.0, $1.25/1K list) is the closest Western model on price — and still costs 8x more than MiMo after normalization.

Where to be cautious

Benchmarks measure capability. Running a model in production depends on more than that.

• Data residency. API calls to Chinese providers may route through infrastructure in China. For regulated industries or teams with data sovereignty requirements, this can be a non-starter. Check each vendor's data processing locations and terms before sending customer data.
• API stability and uptime. OpenAI and Anthropic have years of production API infrastructure and published SLAs. Chinese providers are newer to the global API market. Expect differences in rate limiting, error handling, and availability documentation.
• Reasoning overhead and latency. Six of the eight models in the table above are reasoning models. They may have fast time-to-first-token, but total response time can be significantly longer because the model thinks before answering. For latency-sensitive applications like real-time chat, test actual end-to-end response times — not just benchmarks.
• Context window variation. DeepSeek V3.2's reasoning endpoint has a 32K context window (other V3.2 variants support 128K but score lower). Kimi K2.5 supports 256K. MiMo handles 128K. If your use case involves long documents or extended conversations, check the context limit before committing — the cheapest model won't help if it can't fit your prompt.
• Ecosystem maturity. SDK support, function calling, structured outputs, streaming, and third-party integrations vary significantly. DeepSeek has the most mature developer ecosystem among Chinese labs. Others may require more integration work.
• Content filtering differences. Chinese models may have different content moderation policies. Some topics that work fine with Western APIs may hit filters, and vice versa. Test with your actual production prompts.

The labs to know

A quick reference for the seven Chinese AI labs in our database:

• DeepSeek — Open-weights research lab. V3.2 is their flagship. Known for efficient architectures and open model releases.
• Z AI (Zhipu) — Founded by Tsinghua researchers. GLM model series. GLM-5 is currently the highest-scoring Chinese model in our database.
• Kimi (Moonshot AI) — Known for long-context models. K2.5 supports 256K tokens and scores 88% on GPQA.
• Xiaomi — Consumer electronics giant. MiMo-V2-Flash is their entry — released February 2026, immediately became the cheapest flagship-tier model in our database.
• Alibaba — Qwen model series. Qwen3 Max Thinking scores 39.7 II. Large model lineup but generally priced higher than DeepSeek and Xiaomi.
• MiniMax — Chinese AI startup. M2.1 scores 39.5 II at $0.90 — one of the best value options in the mid-range.
• Baidu — ERNIE model series. ERNIE 5.0 Thinking Preview scores 29.1 II at $2.53 — not yet competitive with the top models above, but Baidu has the distribution advantage of being integrated into China's largest search engine.

How to test a swap

Switching models based on benchmarks alone is a gamble. What you want is a side-by-side comparison on your actual workload — before you ship anything to production.

The MarginDash cost simulator lets you do this: pick a feature, select a Chinese alternative, and see projected cost savings based on your actual usage data. It uses the same AAII normalization described above, and filters out any model that drops more than 10% on benchmarks or can't handle your context window, so you're only comparing viable swaps.

If you're spending $6+ per 1,000 requests on GPT-5 or Claude Sonnet and haven't looked at what DeepSeek V3.2 or MiMo would cost for the same workload, you're likely overpaying by 8–14x or more.

You can explore all 397 models, filter by vendor, and run your own comparisons — sign up free to access the model database and cost simulator.