OpenRouter Model Pricing and Intelligence-per-Dollar Analysis
Model Intelligence and Pricing Comparison
A developer analyzed OpenRouter API pricing for 16 AI models and calculated intelligence-per-dollar values to help select models for specific tasks. The intelligence metric combines seven benchmarks: Artificial Analysis Intelligence Index, Agentic Index, Coding Index, Artificial Analysis Omnicience Index (rescaled to 0-100), GPDval-AA, Terminal-Bench Hard, and t2-Bench Telecom.
Key Findings
The analysis identified several standout models:
- Top intelligence: GPT-5.4 (58.8 intelligence, $2.50/M tokens) and Gemini 3.1 Pro (58.6 intelligence, $2.00/M tokens)
- Best value: MiMo-V2-Flash (39.9 intelligence, $0.09/M tokens, 443 value score)
- Balance models: GLM-5, Kimi K2.5, and Gemini 3 Flash
Model Details and Capabilities
The complete dataset includes:
- MiMo-V2-Flash: 39.9 intelligence, $0.09/M tokens, 443 value, text-only
- Step 3.5 Flash: 34.8 intelligence, $0.10/M tokens, 348 value, general fast text tasks
- Grok 4.1 Fast: 41.2 intelligence, $0.20/M tokens, 205 value, 2M context window, high-speed routing and extraction
- MiniMax M2.5: 40.3 intelligence, $0.27/M tokens, 149 value, open-source, excellent performance in real coding tasks
- DeepSeek V3.2: 34.6 intelligence, $0.25/M tokens, 138 value, strong coding and logic capabilities, supports API cache hits
- Kimi K2.5: 45.8 intelligence, $0.45/M tokens, 101 value, 262K context window, broad general knowledge
- Gemini 3 Flash: 47.7 intelligence, $0.50/M tokens, 95 value, multimodal with audio input support
- GLM-4.7: 31.6 intelligence, $0.38/M tokens, 83 value, general text generation
- Qwen 3.5: 41.1 intelligence, $0.60/M tokens, 68 value, strong overall performance, general purpose
- GLM-5: 49.5 intelligence, $0.80/M tokens, 61 value, 200K context window, general knowledge
- Claude Haiku 4.5: 36.5 intelligence, $1.00/M tokens, 36 value, fast and cheap, extended thinking support
- GPT-5.3: 55.9 intelligence, $1.75/M tokens, 32 value, general reasoning and text processing
- GPT-5.2: 50.8 intelligence, $1.75/M tokens, 29 value, excellent for coding + agentic tasks
- Gemini 3.1 Pro: 58.6 intelligence, $2.00/M tokens, 29 value, multimodal analyses, image output support
- Grok 4.2 Beta: 49.6 intelligence, $2.00/M tokens, 25 value, heavy reasoning, broad knowledge base
- GPT-5.4: 58.8 intelligence, $2.50/M tokens, 24 value, variable context tiers (<272K / >272K), top-tier reasoning
- Claude Sonnet 4.6: 52.3 intelligence, $3.00/M tokens, 17 value, workhorse model, trained through Jan 2026
- Claude Opus 4.6: 51.9 intelligence, $5.00/M tokens, 10 value, top-tier reasoning, strongest for coding and software engineering
Notable Insights
The analysis notes that smarter models typically have worse value scores, but this may not reflect actual efficiency. For example, if Qwen 3.5 uses 500,000 tokens and 30 minutes to solve a problem incorrectly while Sonnet solves it correctly in one-tenth the time, Sonnet might be better value despite its lower intelligence-per-dollar score.
Grok 4.1's 2M context window gives it an intelligence boost that won't appear in most use cases. MiniMax 2.5 outperforms it on all metrics except context window.
GLM-5 marks the last model before a significant value drop (from 61 to 36 with Claude Haiku 4.5) and is reportedly almost as smart as GPT-5.2.
📖 Read the full source: r/openclaw
👀 See Also
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Applying Claude Code's Architecture to Local 9B Models: Key Findings and Optimizations
A developer extracted architectural patterns from Claude Code's leaked source code and applied 10 optimizations to qwen3.5:9b running locally on an RTX 5070 Ti. The key discovery was that qwen3.5:9b has native structured tool_calls, and the biggest limitation for 9B models is self-discipline in knowing when to stop exploring and start producing output.
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