Top picks for Trivia & General Knowledge (2026)
Quick factual answers. Ranked from 405 live models on the OpenRouter catalog, weighted for reasoning quality, low cost, low latency.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | OpenAI: GPT-5.6 Lunaopenai/gpt-5.6-luna | 122 | $0.10 | $0.60 | 1,050,000 | Details → |
| 2 | OpenAI: GPT-5.6 Luna (batch)openai/gpt-5.6-luna:batch | 122 | $0.10 | $0.60 | 1,050,000 | Details → |
| 3 | OpenAI: GPT-5 (batch)openai/gpt-5:batch | 122 | $0.62 | $5.00 | 400,000 | Details → |
| 4 | Z.ai: GLM 5.2 (batch)z-ai/glm-5.2:batch | 122 | $0.70 | $2.20 | 512,000 | Details → |
| 5 | MoonshotAI: Kimi K2.6moonshotai/kimi-k2.6 | 122 | $0.95 | $4.00 | 262,144 | Details → |
| 6 | Z.ai: GLM 5.2z-ai/glm-5.2 | 122 | $0.50 | $3.15 | 1,048,576 | Details → |
| 7 | Anthropic: Claude Opus 4.7 (batch)anthropic/claude-opus-4.7:batch | 121 | $2.50 | $12.50 | 1,000,000 | Details → |
| 8 | Google: Gemini 3.6 Flash (batch)google/gemini-3.6-flash:batch | 121 | $0.75 | $3.75 | 1,048,576 | Details → |
| 9 | Google: Gemini 3.5 Flash (batch)google/gemini-3.5-flash:batch | 121 | $0.75 | $4.50 | 1,048,576 | Details → |
| 10 | MiniMax: MiniMax M3 (batch)minimax/minimax-m3:batch | 121 | $0.15 | $0.60 | 524,288 | Details → |
| 11 | MiniMax: MiniMax M3minimax/minimax-m3 | 121 | $0.30 | $1.20 | 1,048,576 | Details → |
| 12 | Anthropic: Claude Sonnet 4.6 (batch)anthropic/claude-sonnet-4.6:batch | 121 | $1.50 | $7.50 | 1,000,000 | Details → |
| 13 | MoonshotAI: Kimi K2.7 Code (batch)moonshotai/kimi-k2.7-code:batch | 120 | $0.47 | $2.00 | 262,144 | Details → |
| 14 | MoonshotAI: Kimi K2.5moonshotai/kimi-k2.5 | 120 | $0.57 | $2.85 | 262,144 | Details → |
| 15 | Upstage: Solar Pro 4upstage/solar-pro4 | 120 | $0.03 | $0.12 | 524,288 | Details → |
How we ranked these
For Trivia & General Knowledge, we weight models on reasoning quality, low cost, low latency. 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 Trivia & General Knowledge
Trivia and General Knowledge is the task of retrieving and delivering accurate factual answers to straightforward questions across any domain. You need this when your application requires fast, single-fact responses without requiring reasoning, synthesis, or multi-step problem-solving. Models perform well on this task when they have broad training data coverage and can distinguish between common facts and plausible-sounding falsehoods. Poor performance typically stems from hallucination, outdated training cutoffs, or confusion between similar entities. The main practical tradeoff: smaller models are faster and cheaper but make more factual errors, while larger models like GPT-4 are more reliable but incur higher per-query costs.
When to use: Use this when you need quick answers to straightforward factual questions like "What is the capital of France?" or "Who won the 2022 World Cup?" without requiring explanation or context.
Common questions
Which AI models are most accurate for trivia questions?
GPT-4 and Claude 3.5 Sonnet consistently rank highest for factual accuracy on trivia tasks, with accuracy rates above 90% on standard benchmarks. If cost is critical, GPT-4o Mini or Llama 3.1 offer solid performance at lower price points, though with slightly higher error rates on obscure questions.
How fast do trivia models need to be?
Most trivia applications need responses within 1-3 seconds since users expect instant answers. API latency from GPT-4 or Claude typically ranges 500-2000ms, which is acceptable, while local models like Llama can respond in under 500ms if you have sufficient hardware.