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Model shortlist

Best long-context model APIs for large documents

Compare long-context model APIs by context window, price, model source, and recommended document-heavy use cases.

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What is this shortlist for?

Long-context models are useful when prompts include full contracts, knowledge-base exports, support histories, or large code files. The tradeoff is that longer prompts can quickly increase cost, so teams should compare both context window and input price before shipping.

Source basis: NextModel curated catalog and OpenRouter context metadata when available. · Updated 2026-07-01

Context

Recommended candidates long-context models

Start with the shortlist, then test real prompts and compare monthly cost before production routing.

GoogleCatalog

Google: Gemini 2.5 Pro

91

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

$1.25 / 1M tokensInput$10 / 1M tokensOutput1MContext
Best forlong-context analysis, vision workflows, scientific reasoning
RoutingConfigured
Tool callingVisionJSON modeLong contextReasoningStreaming
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
GoogleCatalog

Google: Gemini 2.5 Flash

86

Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...

$0.3 / 1M tokensInput$2.50 / 1M tokensOutput1MContext
Best forlong-document summarization, image Q&A, fast multimodal routing
RoutingConfigured
Tool callingVisionJSON modeLong contextStreamingLow cost
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
MetaCatalog

Meta: Llama 4 Maverick

81

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...

$0.15 / 1M tokensInput$0.6 / 1M tokensOutput1MContext
Best foropen-model workflows, cost-sensitive long context, classification
RoutingConfigured
JSON modeLong contextStreamingLow costTool callingVision
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details
AnthropicCatalog

Anthropic: Claude Opus 4.7

96

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...

$5 / 1M tokensInput$25 / 1M tokensOutput1MContext
Best forfrontier reasoning, large codebase review, strategy analysis
RoutingConfigured
Tool callingJSON modeLong contextReasoningStreamingVision
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
View details

Comparison table

Compare the shortlist by price, provider, context, capability, and source.

Use this view when you're narrowing a production shortlist, building a fallback policy, or comparing model economics.

ModelProviderInputOutputContextCapabilitiesBest forLatencyStatusSource
Google: Gemini 2.5 Progoogle/gemini-2.5-proGoogle$1.25 / 1M tokens$10 / 1M tokens1M
Tool callingVisionJSON modeLong context
long-context analysis, vision workflows1500-5000msCatalogOpenRouter if available
Google: Gemini 2.5 Flashgoogle/gemini-2.5-flashGoogle$0.3 / 1M tokens$2.50 / 1M tokens1M
Tool callingVisionJSON modeLong context
long-document summarization, image Q&A900-2800msCatalogOpenRouter if available
Meta: Llama 4 Maverickmeta-llama/llama-4-maverickMeta$0.15 / 1M tokens$0.6 / 1M tokens1M
JSON modeLong contextStreamingLow cost
open-model workflows, cost-sensitive long context950-2800msCatalogOpenRouter if available
Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7Anthropic$5 / 1M tokens$25 / 1M tokens1M
Tool callingJSON modeLong contextReasoning
frontier reasoning, large codebase review2300-6800msCatalogOpenRouter if available
Anthropic: Claude Sonnet 4.5anthropic/claude-sonnet-4.5Anthropic$3 / 1M tokens$15 / 1M tokens1M
Tool callingJSON modeLong contextReasoning
coding agents, code review1600-4800msCatalogOpenRouter if available
Qwen: Qwen3 Coder Plusqwen/qwen3-coder-plusAlibaba Cloud / Qwen$0.65 / 1M tokens$3.25 / 1M tokens1M
Tool callingJSON modeLong contextStreaming
Chinese engineering workflows, code generation1200-3900msCatalogOpenRouter if available
DeepSeek V4 Flashdeepseek-v4-flashDeepSeek$0.112 / 1M tokens$0.224 / 1M tokens128k
Tool callingJSON modeLong contextReasoning
low-cost Chinese tasks, long-context summary700-2200msCatalogOpenRouter if available
Kimi K2.6kimi-k2-6Moonshot AI$0.73 / 1M tokens$3.49 / 1M tokens128k
JSON modeLong contextStreaming
long Chinese documents, contract review1000-3200msCatalogOpenRouter if available

FAQ

Long-context models FAQ

Is a larger context window always better?

No. Larger context helps with big inputs, but cost, latency, retrieval design, and answer quality still matter.

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