Long-Context Model Selector

Find models that clear a large context requirement while preserving price, output, and provider constraints.

Interactive Tool

Build a Scenario

Runs in your browser

Required contextModels with smaller or unknown windows are excluded.Minimum outputRequired sourced maximum output capacity.Maximum input price / 1M$Use zero for no price ceiling.Monthly input tokensUsed for the workload cost estimate.Monthly output tokensUsed for the workload cost estimate.

Ranking priority

Changes weights, not the eligibility gate.

Result	Value	How to read it
Eligible models	75	Models that pass the selected evidence and constraint gates.
Ranked shortlist	20	Top candidates shown below.
Priced candidates	66	Models with both input and output price minima.

Rank	Model	Fit score	Context	Input / 1M	Workload cost	Published benchmark rank	Providers
1	GLM 5.3 FlashZ.ai	55.45	1M tokens	$0.08 / 1M	$12.50	Unknown	4
2	Qwen3.8 FlashQwen	55.43	1M tokens	$0.11 / 1M	$18.94	Unknown	4
3	Qwen3.7 PlusQwen	55.28	1M tokens	$0.32 / 1M	$57.60	Unknown	4
4	GLM 5.3Z.ai	54.86	1M tokens	$0.90 / 1M	$170.00	Unknown	4
5	Qwen3.7 MaxQwen	54.61	1M tokens	$1.48 / 1M	$236.00	Unknown	4
6	Qwen3.8 MaxQwen	54.5	1M tokens	$1.65 / 1M	$264.02	Unknown	4
7	DeepSeek V4.1 FlashDeepSeek	53.9	1.05M tokens	$0.15 / 1M	$27.00	Unknown	3
8	DeepSeek V4 Flash Vision ExpDeepSeek	53.87	1.05M tokens	$0.22 / 1M	$35.20	Unknown	3
9	Claude Sonnet 5Anthropic	52.48	1M tokens	$2.00 / 1M	$400.00	Unknown	3
10	Qwen3.7 FlashQwen	52.48	1M tokens	$0.03 / 1M	$5.60	Unknown	2
11	GPT-4.1 NanoOpenAI	52.47	1.05M tokens	$0.05 / 1M	$9.00	Unknown	2
12	Gemini 2.5 Flash LiteGoogle DeepMind	52.43	1.05M tokens	$0.10 / 1M	$18.00	Unknown	2
13	GPT-6 LunaOpenAI	52.42	1.05M tokens	$0.10 / 1M	$20.00	Unknown	2
14	GPT-4.1 MiniOpenAI	52.36	1.05M tokens	$0.20 / 1M	$36.00	Unknown	2
15	GPT-5.6 LunaOpenAI	52.33	1.05M tokens	$0.20 / 1M	$44.00	Unknown	2
16	Gemini 3.1 Flash LiteGoogle DeepMind	52.29	1.05M tokens	$0.25 / 1M	$55.00	Unknown	2
17	MiMo V2.6 ProXiaomi	52.27	1.05M tokens	$0.44 / 1M	$60.90	Unknown	2
18	Gemini 2.5 FlashGoogle DeepMind	52.2	1.05M tokens	$0.30 / 1M	$80.00	Unknown	2
19	Gemini 3.5 Flash LiteGoogle DeepMind	52.2	1.05M tokens	$0.30 / 1M	$80.00	Unknown	2
20	Gemini 3 FlashGoogle DeepMind	52.08	1.05M tokens	$0.50 / 1M	$110.00	Unknown	2

Fit scores are transparent screening aids, not universal quality claims. Unknown capability, context, or price fields are never filled by inference.

Inputs

What the Calculation Needs

3 input groups

Input	How it is used
Required context	Full input, output, overhead, and safety envelope.
Minimum output	Response capacity required after the prompt fits.
Maximum input price	Optional screening ceiling for long-context input.

Methodology

Unknown context limits fail closed. Passing models receive a headroom ratio and are ranked using the selected priority plus sourced price and provider breadth.

How to Interpret the Result

Large nominal windows can have different retrieval quality, latency, and provider limits. Test the full workload, not only whether it fits numerically.

Boundaries

What the Result Does Not Prove

Provider endpoints can expose less context than the base model.
Long prompts can produce nonlinear latency and cost.
Context capacity is not evidence of long-context recall quality.

Catalog values retain their source and freshness on the linked model, provider, benchmark, or comparison page. Editable scenario assumptions are not Model Markets measurements.

Continue the Analysis

Related Tools

All tools →

Tool	Next question
Context Window	Turn a nominal context-window number into a practical fit check for a complete request envelope.
Model Selector	Turn workload constraints into a short, inspectable model shortlist instead of a universal best-model claim.
Token Counter	Get a fast planning estimate before sending a prompt or document to a tokenizer-backed API.

Questions

Long-Context Models FAQs

What does the Long-Context Model Selector calculate?+

Filter the catalog by usable request-envelope requirements before comparing cost or benchmark rank. It returns passing models ordered by context headroom, output capacity, price, and provider coverage.

Does the Long-Context Model Selector use current model data?+

Where the calculation needs model facts, it uses the current Model Markets catalog snapshot updated 2026-10-19. User-entered assumptions remain clearly editable, and unsupported values stay unknown rather than being inferred.

What should I verify before using the Long-Context Model Selector result?+

Provider endpoints can expose less context than the base model. Long prompts can produce nonlinear latency and cost. Context capacity is not evidence of long-context recall quality. Open the linked canonical records and primary sources before making a production or purchasing decision.