{"catalog_id": "feegate-ai-model-value-rank", "product_name": "AI 大模型选型价值排名（value_score 智能排名）", "updated_at": "2026-11-09T19:07:04Z", "source": "https://raw.githubusercontent.com/Yotoha0303/Yotoha0303/v2.0.0-feegate-release/data/feegate-ai-model-pricing-index.json", "source_provenance": {"source_origin": "OpenRouter Official Public Model Registry", "source_endpoint": "https://openrouter.ai/api/v1/models", "ingested_at_utc": "2026-11-05T06:36:12.071234+00:00", "schema_version": "2.0.0", "record_count": 455, "sha256_checksum": "df4cf8e3d536d6328c4a938be25c7146d2a1c7fc33037f6a9b3ff719bbb24b1d"}, "record_count": 448, "skipped_no_pricing": 7, "methodology": {"value_score": "100 * (0.5*cost_eff + 0.5*ctx_eff)，四舍五入 2 位", "cost_eff": "min(1, 0.15 / blended_3to1_cost)，blended=0（免费模型）记满分；$0.15/M 以下成本维度满分", "ctx_eff": "min(1, log10(context/32768) / log10(1048576/32768))，32K 上下文记 0 分、1M 记满分（对数缩放）", "blended_3to1_cost": "每百万 token 混合成本 = (prompt + 3*completion) / 4，业界通用的 3:1 输入输出比折算", "why": "把「成本」与「上下文容量」压成一个可比分数：同一个分数下，便宜的长上下文模型得分高；适合按预算快速圈定候选再细看。", "recompute": "每条记录带 cost_eff/ctx_eff 原始值与公式，任何人都可按 source 链接拉原始数据复算。", "limitations": "不含能力/质量维度（benchmark 分数不在原始数据里），value_score 是「性价比」而非「最强」排名。"}, "records": [{"model_id": "~z-ai/glm-flash-latest", "name": "Z.ai: GLM Flash Latest", "provider": "~z-ai", "context_window_tokens": 1048576, "blended_cost_usd_per_1m": 0.0445, "prompt_cost": 0.0285, "completion_cost": 0.0923, "modality": "text+image+video->text", "cost_eff": 1.0, "ctx_eff": 1.0, "value_score": 100.0, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "z-ai/glm-5.3-flash:batch", "name": "Z.ai: GLM 5.3 Flash (batch)", "provider": "z-ai", "context_window_tokens": 1048576, "blended_cost_usd_per_1m": 0.0825, "prompt_cost": 0.05, "completion_cost": 0.18, "modality": "text+image+video->text", "cost_eff": 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log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "google/gemini-2.5-flash-image", "name": "Google: Nano Banana (Gemini 2.5 Flash Image)", "provider": "google", "context_window_tokens": 32768, "blended_cost_usd_per_1m": 0.85, "prompt_cost": 0.3, "completion_cost": 2.5, "modality": "text+image->text+image", "cost_eff": 0.1765, "ctx_eff": 0.0, "value_score": 8.82, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "moondream/moondream3-preview", "name": "Moondream: Moondream 3 Preview", "provider": "moondream", "context_window_tokens": 32000, "blended_cost_usd_per_1m": 0.85, "prompt_cost": 0.3, "completion_cost": 2.5, "modality": "text+image->text", "cost_eff": 0.1765, "ctx_eff": 0.0, "value_score": 8.82, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "moondream/moondream3.1-9b-a2b", "name": "Moondream: Moondream 3.1", "provider": "moondream", "context_window_tokens": 32000, "blended_cost_usd_per_1m": 0.85, "prompt_cost": 0.3, "completion_cost": 2.5, "modality": "text+image->text", "cost_eff": 0.1765, "ctx_eff": 0.0, "value_score": 8.82, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "aion-labs/aion-rp-llama-3.1-8b", "name": "AionLabs: Aion-RP 1.0 (8B)", "provider": "aion-labs", "context_window_tokens": 32768, "blended_cost_usd_per_1m": 1.0, "prompt_cost": 0.8, "completion_cost": 1.6, "modality": "text->text", "cost_eff": 0.15, "ctx_eff": 0.0, "value_score": 7.5, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "openai/gpt-3.5-turbo-0613", "name": "OpenAI: GPT-3.5 Turbo (older v0613)", "provider": "openai", "context_window_tokens": 4095, "blended_cost_usd_per_1m": 1.25, "prompt_cost": 1.0, "completion_cost": 2.0, "modality": "text->text", "cost_eff": 0.12, "ctx_eff": 0.0, "value_score": 6.0, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "anthracite-org/magnum-v4-72b", "name": "Magnum v4 72B", "provider": "anthracite-org", "context_window_tokens": 32768, "blended_cost_usd_per_1m": 3.125, "prompt_cost": 2.5, "completion_cost": 5.0, "modality": "text->text", "cost_eff": 0.048, "ctx_eff": 0.0, "value_score": 2.4, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "openai/gpt-3.5-turbo-16k", "name": "OpenAI: GPT-3.5 Turbo 16k", "provider": "openai", "context_window_tokens": 16385, "blended_cost_usd_per_1m": 3.25, "prompt_cost": 3.0, "completion_cost": 4.0, "modality": "text->text", "cost_eff": 0.0462, "ctx_eff": 0.0, "value_score": 2.31, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}, {"model_id": "openai/gpt-4", "name": "OpenAI: GPT-4", "provider": "openai", "context_window_tokens": 8191, "blended_cost_usd_per_1m": 37.5, "prompt_cost": 30.0, "completion_cost": 60.0, "modality": "text->text", "cost_eff": 0.004, "ctx_eff": 0.0, "value_score": 0.2, "score_components": {"cost_eff_formula": "min(1, 0.15 / blended_cost)", "ctx_eff_formula": "min(1, log10(ctx/32768) / log10(1048576/32768))", "value_score_formula": "100 * (0.5*cost_eff + 0.5*ctx_eff)"}}], "insight": "448 款模型中 15 款 value_score 满分（成本 < $0.15/M 且上下文接近 1M），196 款上下文 ≥ 1M，16 款为零定价/免费模型；付费模型中 blended 成本中位 $0.88/M，最低 openai/gpt-oss-120b（$0.0173/M）；满分榜由 ~z-ai/glm-flash-latest 领跑——「便宜的长上下文」已是红海。"}