Understanding AI model pricing and performance is essential when choosing between models for OpenClaw or other local agent frameworks comes down to three things: how smart the model is, how fast it responds, and how much it costs. The gap between the cheapest and most expensive models is now 200x or more. This AI model pricing guide compares the top Western and Chinese models across cost, speed, intelligence, and context window so you can pick the right one for your workflow.
If you run OpenClaw or a similar agent framework, you have probably wondered which model to set as your primary, which to keep as a fallback, and whether paying for a premium model is actually worth it. The answer changes every few weeks as new models launch and prices shift.
I spent time gathering pricing and benchmark data from official provider pages, Artificial Analysis, and OpenRouter to build a picture of where things stand in May 2026. The landscape includes models from the US (OpenAI, Anthropic, Google, xAI) and increasingly from China (DeepSeek, Alibaba Qwen, Moonshot Kimi, Zhipu AI GLM, MiniMax, 01.AI). The Chinese players are not just cheap alternatives anymore. Some of them lead the intelligence rankings.
This guide covers the key metrics for each model family, compares Western and Chinese approaches, and gives specific OpenClaw configuration recommendations for different use cases.
What Matters for Agent Workloads
Agent frameworks like OpenClaw make repeated API calls in loops. A single user request might trigger 5 to 15 model calls as the agent reasons, calls tools, checks results, and refines its output. That changes what you should optimize for.
Intelligence determines whether the agent handles complex tasks correctly. A model that scores 55 on the Artificial Analysis Intelligence Index will solve most problems, but a model scoring 45 might get confused on multi-step reasoning. For simple tasks like summarization or classification, even low-intelligence models work fine.
Output speed (tokens per second) determines how long the user waits. For interactive agents, you want this above 100 t/s. For batch processing, even 40 t/s works if you are running 20 parallel calls.
Latency (time to first token) and throughput are different things. Low latency matters for chat-like interactions. High throughput matters for processing large volumes in the background.
Context window size matters when your agent carries long conversation histories or processes large documents. A 1M token window means you can feed an entire codebase or a book into the context without chunking.
Price determines how many agents you can run and how often. A model that costs $0.28 per million output tokens lets you run hundreds of sessions for what a premium model costs for a single session.
Western Models: The Incumbents
OpenAI: GPT-5.5 and the GPT Family
OpenAI remains the reference point, but its lead has narrowed. GPT-5.5 (xhigh) scores 60.2 on the intelligence index, making it the smartest model available. It comes at a premium: $5 per million input tokens and $30 per million output tokens. Speed is 71 tokens per second.
GPT-5.1 (high) offers a better balance at $1.25 input and $10 output with 114 t/s speed and an intelligence score of 48. GPT-5 (medium) is similar at $1.25 input and $10 output with 90 t/s and an intelligence score of 42.
The GPT-OSS line is something new. OpenAI released GPT architecture as open weights, and Groq runs it on LPU hardware at extreme speeds. GPT-OSS 20B hits 1,000 tokens per second at $0.075 input and $0.30 output. GPT-OSS 120B hits 500 t/s at $0.15 input and $0.60 output. These are among the best values for agent workloads.
All OpenAI models support prompt caching with 75% to 90% discounts on cache hits.
Anthropic: Claude Opus 4.7 and Sonnet 4.6
Anthropic positions Claude as the best model for agentic coding. Opus 4.7 (max) scores 57.3 on the intelligence index, ranking second overall. Pricing is steep: $6.25 input and $25 output per million tokens for Opus 4.7. Sonnet 4.6 costs $3.75 input and $15 output, with an intelligence score of 44 and speed of 48 t/s. Haiku 4.5 costs $1 input and $5 output.
Both Opus and Sonnet support 1M token context windows. Cache discounts reach 92%. Claude excels at following complex instructions and handling multi-turn agent workflows. The tradeoff is speed. Opus 4.7 runs at 54 t/s and Sonnet 4.6 at 48 t/s.
Google: Gemini 3.5 Flash and the Gemini Family
Gemini 3.5 Flash is Google’s standout. It scores 55 on the intelligence index (eighth overall), runs at 204 t/s (third fastest among frontier models), and supports a 1.05M token context window. Pricing is $1.50 input and $9 output per million tokens.
Gemini 3.1 Flash-Lite costs $0.25 input and $1.50 output. Gemini 2.5 Pro costs $1.25 input and $10 output with 146 t/s speed. Gemini 2.5 Flash costs $0.30 input and $2.50 output with 225 t/s.
Google’s advantage is multimodal support. Every Gemini model accepts text, images, video, audio, and PDF natively. Gemini 3.5 Flash also offers 5,000 free prompts per month through Google AI Studio.
xAI: Grok 4.3
Grok 4.3 (high) scores 53 on the intelligence index and runs at 165 t/s. Pricing is $1.25 input and $2.50 output, making it one of the best price-to-performance ratios among Western premium models. Cache discounts reach 84%.
Chinese Models: The Rising Force
Chinese AI labs are no longer just producing cheaper alternatives. Several Chinese models now rank among the top 10 in intelligence, and their pricing is often dramatically lower than Western equivalents.
DeepSeek: V4 Flash and V4 Pro
DeepSeek has become the most talked about Chinese AI company. Their models combine near-frontier intelligence with pricing that is 10x to 200x cheaper than equivalent Western models.
DeepSeek V4 Flash is the best value in AI right now. It scores 47 on the intelligence index (tenth among open-weight models), runs at 137 t/s, and supports 1M token context. Pricing is $0.14 input and $0.28 output per million tokens. Cache hits reduce input cost to $0.0028.
DeepSeek V4 Pro scores 52 on the intelligence index (third among open-weight models). Official pricing is currently $0.435 input and $0.87 output thanks to a 75% discount promotion that ends May 31, 2026. After that, the standard prices of $1.74 input and $3.48 output apply.
Both models are open-weight, meaning you can self-host them or use them through API providers. This is a significant advantage for avoiding vendor lock-in.
Alibaba Qwen Series
Alibaba’s Qwen team has been shipping models at an impressive pace. The series spans from tiny 9B parameter models to massive MoE architectures.
Qwen3.7 Max is the flagship, scoring 57 on the intelligence index (tied for fourth overall). It runs at 208 t/s and costs $1.25 input and $3.75 output. It is designed for agent-centric workloads with strengths in coding, office automation, and long-horizon tasks.
Qwen3.6 Plus costs $0.325 input and $1.95 output with 1M context. Qwen3.5 Flash costs just $0.065 input and $0.26 output, making it one of the cheapest capable models available. All Qwen models are open-weight under Apache 2.0 license.
Moonshot AI: Kimi K2.6
Kimi K2.6 scores 54 on the intelligence index (first among open-weight models, fourth overall). It is a 1 trillion parameter MoE model with 32B active parameters. Pricing is $0.95 input and $4.00 output. It supports image and video input in addition to text, with a 256K token context window.
Zhipu AI: GLM-5.1
GLM-5.1 from Zhipu AI scores 51 on the intelligence index (fourth among open-weight models). It uses 744B total parameters with 40B active. Pricing is $1.40 input and $4.40 output. Context window is 200K tokens. Speed is 61 t/s.
MiniMax: M2.7 and M2.5
MiniMax M2.7 is optimized for real-world productivity and multi-agent collaboration. Pricing is $0.279 input and $1.20 output. MiniMax M2.5 costs $0.15 input and $1.15 output and scores 80.2% on SWE-Bench Verified. It is available as a free tier through OpenRouter.
01.AI: MiMo-V2.5-Pro
MiMo-V2.5-Pro is a 1 trillion parameter MoE model from 01.AI (founded by Kai-Fu Lee). It scores 54 on the intelligence index (second among open-weight models). Pricing is $0.90 input and $2.70 output. Context window is 1M tokens.
Western vs Chinese: Head to Head
Intelligence. Chinese models have closed the gap significantly. Four Chinese models rank in the top 10: Kimi K2.6 (#4), MiMo-V2.5-Pro (#5), DeepSeek V4 Pro (#8), and GLM-5.1 (#9). The top models are still Western (GPT-5.5, Claude Opus 4.7), but the margin is small.
Pricing. Chinese models win decisively. DeepSeek V4 Flash at $0.14 input is roughly 35x cheaper than GPT-5.5 on input and 107x cheaper on output. Even at the premium tier, Qwen3.7 Max at $1.25 input is 4x cheaper than Claude Opus 4.7 at $6.25.
Speed. Qwen3.7 Max runs at 208 t/s, faster than any Western premium model except Gemini 3.5 Flash (204 t/s). DeepSeek V4 Flash at 137 t/s is competitive with GPT-5.1 (114 t/s).
Context Window. DeepSeek V4 Flash and Pro, Qwen3.7 Max, Qwen3.5 Flash, and MiMo-V2.5-Pro all support 1M token contexts. Only Gemini’s 1.05M and Meta’s Llama 4 Scout (10M) are larger.
Open Weights. DeepSeek, Qwen, Kimi, GLM, MiniMax, and MiMo all release open-weight models. OpenAI and Anthropic keep their best models proprietary. Google and xAI do not release weights for their top models either.
AI Model Pricing: Per-Session Cost Comparison for Agent Workloads
Here is what a typical agent session costs. Assume 10 turns, each with a 2,000 token system prompt (cached) and 500 token response.
| Model | Total Input | Total Output | Cost Per Session | Cost for 500 Sessions |
|---|---|---|---|---|
| GPT-5.5 (xhigh) | 7,000 tokens | 5,000 tokens | $0.185 | $92.50 |
| Claude Opus 4.7 | 7,000 tokens | 5,000 tokens | $0.168 | $84.00 |
| GPT-5.1 (high) | 7,000 tokens | 5,000 tokens | $0.059 | $29.50 |
| Gemini 3.5 Flash | 7,000 tokens | 5,000 tokens | $0.056 | $28.00 |
| DeepSeek V4 Flash | 7,000 tokens | 5,000 tokens | $0.002 | $1.00 |
| Qwen3.5 Flash | 7,000 tokens | 5,000 tokens | $0.0018 | $0.90 |
| Kimi K2.6 | 7,000 tokens | 5,000 tokens | $0.027 | $13.50 |
| Groq Llama 3.1 8B | 7,000 tokens | 5,000 tokens | $0.0007 | $0.35 |
The gap between DeepSeek V4 Flash at $1 per 500 sessions and GPT-5.5 at $92.50 is hard to ignore. For most agent workloads, the cheaper model does the job.
OpenClaw Configuration Examples
For maximum quality on hard tasks, use GPT-5.5 with Claude Opus and Gemini as fallbacks. For the best value, use DeepSeek V4 Flash with Gemini 3.5 Flash and Claude Sonnet as fallbacks. For fastest response times, use Groq Llama 3.1 8B with Gemini Flash Lite and Qwen3-32B as fallbacks. For a Chinese-first stack, use DeepSeek V4 Flash with Qwen3.7 Max and Kimi K2.6 as fallbacks.
When to Use Premium (Western) Models
Premium models are worth the cost in specific situations. Use Claude Opus 4.7 or GPT-5.5 when you need to debug a complex issue, write production code, or analyze something that requires deep reasoning. Use Gemini 3.5 Flash when your agent needs to process varied input types. Use DeepSeek V4 Flash or Pro for everything else.
Future Outlook
The AI model pricing trend is clear. Chinese models are catching up on intelligence while maintaining a cost advantage that shows no signs of narrowing. DeepSeek’s strategy of releasing open-weight models at aggressive prices has forced competitors to respond. OpenClaw’s model configuration system makes it easy to adapt to this changing landscape.
Frequently Asked Questions
Q: Which model has the best AI model pricing for OpenClaw agent tasks?
A: DeepSeek V4 Flash at $0.14 input and $0.28 output per million tokens. It scores 47 on the intelligence index, runs at 137 t/s, and supports 1M token context. For most agent tasks, it matches premium models at 1% of the cost.
Q: Are Chinese AI models as good as Western ones?
A: On intelligence benchmarks, the gap has narrowed to single-digit percentage points. Kimi K2.6, Qwen3.7 Max, and DeepSeek V4 Pro all rank in the top 10. Chinese models lead on open-weight availability and price. Western models lead on multimodal support (Google Gemini) and specific capabilities like Claude’s agentic coding.
Q: What context window do I need for an agent?
A: For most agents, 128K to 256K is sufficient. For agents that process entire codebases or lengthy documents, aim for 1M tokens. DeepSeek V4 Flash, Qwen3.7 Max, and Claude Opus 4.7 all support 1M.
Q: Can I run these models locally instead of using APIs?
A: Yes, for open-weight models. DeepSeek V4, Qwen3.5 (9B to 35B), and MiniMax M2.1 can run on consumer hardware with enough RAM. Full-size models like DeepSeek V4 Pro (1.6T parameters) require server-grade hardware.
Q: How do I set up model fallbacks in OpenClaw?
A: Add a fallbacks list under the model config in your OpenClaw config. OpenClaw tries the primary model first and falls back automatically if the API fails or rate limits.
Q: Which provider has the best speed for real-time agents?
A: Groq delivers the fastest speeds, with GPT-OSS 20B at 1,000 t/s and Llama 3.1 8B at 840 t/s. For premium quality with good speed, Qwen3.7 Max at 208 t/s and Gemini 3.5 Flash at 204 t/s are the top choices.
Related: running models locally and running a business on OpenClaw.
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