Top picks for RFP Response (2026)
Long-form proposal answers. Ranked from 334 live models on the OpenRouter catalog, weighted for context window, reasoning quality, structured output.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 192 | $3.00 | $15.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 192 | $5.00 | $25.00 | 1,000,000 | Details → |
| 3 | OpenAI: GPT-5.4openai/gpt-5.4 | 183 | $2.50 | $15.00 | 1,050,000 | Details → |
| 4 | Z.ai: GLM 5.2z-ai/glm-5.2 | 181 | $0.98 | $3.08 | 1,048,576 | Details → |
| 5 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 180 | $5.00 | $25.00 | 1,000,000 | Details → |
| 6 | DeepSeek: DeepSeek V4 Prodeepseek/deepseek-v4-pro | 179 | $0.43 | $0.87 | 1,048,576 | Details → |
| 7 | Google: Gemini 3.1 Pro Previewgoogle/gemini-3.1-pro-preview | 178 | $2.00 | $12.00 | 1,048,576 | Details → |
| 8 | OpenAI: GPT-5.5openai/gpt-5.5 | 177 | $5.00 | $30.00 | 1,050,000 | Details → |
| 9 | DeepSeek: DeepSeek V4 Flashdeepseek/deepseek-v4-flash | 177 | $0.09 | $0.18 | 1,048,576 | Details → |
| 10 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 172 | $1.50 | $9.00 | 1,048,576 | Details → |
| 11 | MiniMax: MiniMax M3minimax/minimax-m3 | 169 | $0.30 | $1.20 | 1,048,576 | Details → |
| 12 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 169 | $0.66 | $3.41 | 262,144 | Details → |
| 13 | Xiaomi: MiMo-V2.5-Proxiaomi/mimo-v2.5-pro | 168 | $0.43 | $0.87 | 1,048,576 | Details → |
| 14 | Qwen: Qwen3.7 Maxqwen/qwen3.7-max | 167 | $1.25 | $3.75 | 1,000,000 | Details → |
| 15 | OpenAI: GPT-5.4 Miniopenai/gpt-5.4-mini | 167 | $0.75 | $4.50 | 400,000 | Details → |
How we ranked these
For RFP Response, we weight models on context window, reasoning quality, structured output. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About RFP Response
An RFP response task requires an AI model to generate long-form proposal answers that directly address client requirements, evaluation criteria, and technical specifications outlined in a Request for Proposal. You need this when responding to government contracts, enterprise vendor selections, or competitive bids where thoroughness and compliance matter more than speed. Good models excel at: maintaining document structure, cross-referencing requirements systematically, synthesizing complex information into coherent narratives, and avoiding redundancy across 20+ page responses. Poor performers lose track of specific requirements mid-document, repeat themselves, or generate generic filler. The practical constraint is token cost: a single RFP response can consume 50K-150K tokens, making batch processing expensive and Claude 3.5 Sonnet or GPT-4o more economical per dollar than smaller models when accuracy is weighted against total spend.
When to use: Use this when you need to draft or complete government bids, enterprise software vendor proposals, or multi-section responses to structured procurement documents where accuracy and requirement traceability directly impact your chances of winning.
Common questions
What is the difference between an RFP response and other proposal writing tasks?
An RFP response specifically answers pre-written evaluation criteria and mandatory sections defined by the buyer, whereas general proposal writing starts from scratch. RFP tasks demand requirement-by-requirement compliance mapping and often include structured scoring rubrics that the model must align with. Claude 3.5 Sonnet and GPT-4 Turbo both handle this well, but GPT-4 Turbo tends to maintain better section numbering consistency across 30+ page documents.
How much does it cost to generate a full RFP response with AI compared to hiring a proposal writer?
A single RFP response (80-120 pages) costs $3-8 in API tokens with GPT-4o or Claude 3.5 Sonnet; a freelance proposal writer charges $3,000-8,000 for the same work. AI excels at speed (4-6 hours vs. 2-3 weeks) and handles updates cheaply, but requires subject-matter expert review to ensure technical accuracy and competitive positioning that humans provide inherently.