Long context work means feeding a model entire codebases, contract bundles, research archives, or hours of transcripts. Those tasks punish models in two ways. Input costs climb fast when every request carries hundreds of thousands of tokens, and quality drops when a model loses track of details buried deep in the prompt. The five models below are the ones we would shortlist for this kind of work. Our data does not list exact token limits for each model, so confirm the current context ceiling on the provider's model page before you commit a pipeline to any of them.
How Input Pricing Shapes Long Context Costs
Input price matters more than output price in long context work. A typical request might send 500,000 tokens of source material and get back a 5,000 token summary. Here is what that single request costs at list prices:
| Model | Input $/Mtok | Output $/Mtok | Cost per 500k in / 5k out | Quality score |
|---|---|---|---|---|
| Meta: Llama 4 Scout | 0.10 | 0.30 | about $0.05 | 99.8 |
| GPT-6.1 Sol Pro (batch) | 1.00 | 5.00 | about $0.53 | 100.0 |
| SpaceXAI: Grok 4.20 | 1.25 | 2.50 | about $0.64 | 100.0 |
| SpaceXAI: Grok 4.20 Multi-Agent | 1.25 | 2.50 | about $0.64 | 98.1 |
| OpenAI: GPT-6.1 Sol Pro | 2.00 | 10.00 | about $1.05 | 100.0 |
The spread is roughly twentyfold between the cheapest and most expensive option. If you run thousands of these requests a month, that gap decides your budget long before quality differences do.
Meta: Llama 4 Scout
Llama 4 Scout is the value pick, and the numbers make that case clearly. It costs $0.10 per million input tokens and $0.30 per million output tokens, yet it posts a quality score of 99.8, which sits within a fraction of the top models on our list. It is a mixture-of-experts model that activates 17 billion parameters out of 109 billion total, which helps explain the low serving cost. It also supports native multimodal input, so you can mix images with text in the same request.
Use Scout for high volume document processing: bulk summarization, extraction from large file sets, first pass triage of long transcripts, and retrieval workloads where you would rather stuff more context than build an elaborate chunking system. At these prices you can afford to rerun a job or send the full document instead of a trimmed excerpt. Pick something else when a single wrong answer carries real cost and you need the strongest available reasoning on top of the long input.
SpaceXAI: Grok 4.20
Grok 4.20 scores 100 on quality and is priced at $1.25 input and $2.50 output, which makes its output tokens the cheapest among the top scoring models here. SpaceXAI describes it as a reasoning model with fast responses, strong agentic tool calling, strict prompt adherence, and what the vendor calls the lowest hallucination rate on the market. Treat that last claim as the vendor's own description and verify it on your own documents.
The combination of low hallucination and strict instruction following is exactly what you want when asking questions about a long document. A model that invents a clause in a contract or misattributes a quote in a transcript is worse than useless. Grok 4.20 suits interactive analysis of long inputs, agent workflows that call tools while holding a large working context, and any setup where you need the model to obey a strict output format across long sessions.
SpaceXAI: Grok 4.20 Multi-Agent
The Multi-Agent variant runs several agents in parallel to conduct deep research, coordinate tool use, and synthesize findings. It shares the same $1.25 and $2.50 pricing as the standard model but scores slightly lower at 98.1. Choose it for open ended research tasks where splitting the work across agents helps, such as surveying a large body of sources and reconciling what they say. For a single well defined question over one long document, the standard Grok 4.20 scores higher and costs the same, so it is the better default.
OpenAI: GPT-6.1 Sol Pro
GPT-6.1 Sol Pro is the GPT-6.1 Sol model served with its reasoning mode set to pro, which produces higher quality answers on complex tasks. It scores 100 and is the most expensive option here at $2.00 input and $10.00 output. OpenAI's own cost note warns that pro mode spends far more tokens, so the real output bill will run well above what the list price suggests. Reserve it for the hardest problems over long inputs, such as tracing logic across a large codebase or reconciling conflicting terms across a stack of legal documents, where you will pay for deeper reasoning to get the answer right the first time.
OpenAI: GPT-6.1 Sol Pro (batch)
The batch version is the same model at half the price: $1.00 input and $5.00 output, with the same quality score of 100. For long context jobs that do not need an immediate response, such as overnight review of document archives or scheduled codebase audits, batch is the obvious choice over the standard endpoint. Its input price also undercuts Grok 4.20, so for input heavy jobs that can wait, it is the cheapest route to top tier quality on this list.
Which One to Pick
Start with Llama 4 Scout for any high volume long context pipeline and only move up when your evaluations show it missing details that matter. Use Grok 4.20 for interactive work where factual accuracy and format compliance count. Send your hardest analysis to GPT-6.1 Sol Pro, and route it through the batch endpoint whenever the job can tolerate a delay. Before you scale any of these, run a test with your actual document sizes and confirm the context limit on the provider page.