High-Value Alternatives and Regional Model Innovation
The expansion of OpenClaw support to include models from Chinese startups and open-source providers has significantly altered the price-performance ratio for power users.[26, 27]
DeepSeek: The Budget Performance Leader
DeepSeek V3 and the reasoning-optimized DeepSeek R1 have become the “go-to” budget models for the OpenClaw community.[14, 24] Priced at a fraction of the cost of frontier models (approximately $0.27 per million input tokens), DeepSeek offers reasoning capabilities that rival high-end OpenAI models.[14, 17] Users have found DeepSeek particularly proficient for coding tasks and routine email processing.[14, 27] Its OpenAI-compatible API allows for seamless integration into the OpenClaw framework, although some researchers have noted that its prompt-injection resistance is significantly weaker than that of Claude or GPT.[14, 28]
MiniMax and Moonshot Kimi: Specialized Agentic Brains
The MiniMax M2.5 Standard model has gained attention for its exceptional performance in tool-calling benchmarks, scoring 76.8% on the BFCL Multi-Turn benchmark, which is notably higher than Claude Opus 4.6.[29] This model is architected to reduce the number of tool-calling rounds needed to complete complex tasks, directly translating to lower token consumption and increased operational speed.[29] Moonshot AI’s Kimi K2.5 has also been praised for its ability to generate parallel sub-agents to solve complex problems, such as searching through multiple domains simultaneously to compile structured data.[30]
|
Model API
|
SWE-Bench Verified (%)
|
BFCL Tool Calling (%)
|
Context Window
|
Output Price (per 1M)
|
|---|---|---|---|---|
|
Claude Opus 4.6
|
80.8%
|
63.3%
|
1M
|
$75.00 [16, 29]
|
|
MiniMax M2.5
|
80.2%
|
76.8%
|
205K
|
$1.20 [29]
|
|
GPT-5.2
|
80.0%
|
N/A
|
400K
|
$14.00 [29, 31]
|
|
DeepSeek V3
|
N/A
|
N/A
|
128K
|
$1.10 (est.) [14, 28]
|
|
Gemini 3 Pro
|
78.0%
|
61.0%
|
1M
|
$12.00 [29, 31]
|
Local Model Inference: Privacy vs. Performance Realities
For users seeking to avoid the recurring costs and data privacy concerns associated with cloud APIs, OpenClaw provides the option to run models entirely on local hardware through runtimes like Ollama and LM Studio.[1, 32] However, the experience of running “local-first” agents is characterized by significant hardware barriers and technical friction.[33, 34]
Hardware Requirements and VRAM Pressures
The most pervasive issue with local OpenClaw usage is the “cognitively demanding” nature of the framework’s architecture.[9] Unlike a standard chat, which might only require a small amount of memory, OpenClaw’s assembly of system prompts, memories, and tool schemas can cause the context to balloon to 60,000 tokens per interaction.[27] Community experience suggests that models under 30 billion parameters generally struggle with the “tool use and reasoning” necessary to effectively manage OpenClaw’s skills.[27]
To run a capable local model with sufficient context, users have reported the need for professional-grade hardware.[27, 33] A setup utilizing dual NVIDIA RTX 5090s with 64GB of pooled VRAM is capable of achieving approximately 30 tokens per second (TPS) on a 70B model, which is hundreds of times slower than typical cloud APIs but functional for private use.[35] Users with consumer-grade Apple silicon, such as an M1 Max with 32GB of RAM, have reported struggling with the context requirements, particularly when multiple skills are active.[33]
Recommended Local Models and Framework Fixes
Despite the challenges, certain local models have been identified as a “sweet spot” for OpenClaw integration. Qwen3-Coder 32B (via Ollama) is highly recommended for its balance of coding capability and tool-calling reliability.[36] GPT-OSS 120B is praised for its reasoning but is noted to become excessively slow as the context window fills up, making it more suitable for one-off tasks than continuous assistance.[33, 36]
A significant technical hurdle for local users has been the “streaming termination” bug, particularly with models like Qwen 2.5:7b.[12] In these instances, the model fails to send a definitive “done” signal to the Gateway, leaving the messaging channel stuck in a permanent “typing” state.[12] The community fix involves modifying the
openclaw.json config to set stream: false, forcing a non-streaming response that improves reliability at the cost of perceived latency.[12]