Best Long Context LLMs in July 2026
Ranked by advertised context window, an objective spec, with the quality and price data needed to judge whether the headline number is worth it.
Context window is the one dimension on this site that can be ranked purely objectively, and this page does exactly that: models ordered by their advertised maximum context, with quality evidence and pricing shown alongside. Two caveats belong next to any such table. Advertised maximums are creator claims, and a given deployment may serve less than the model theoretically supports.
Second, effective context differs from advertised context: retrieval quality degrades toward the middle of very long windows on most models, and long prompts are billed per token, so a million-token window used carelessly is mostly a large bill. The AA-LCR column shows measured long-context reasoning where a published result exists, which is precisely the evidence that separates a big window from a usable one. The right question is usually the smallest context that fits the workload, not the largest available.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
The ranking
This list is ordered by advertised context window, an objective specification, across the 668 models with a published figure. The ordering makes no quality claim; the quality index is shown for context where measured evidence exists. Advertised maximums are what creators publish, and a given deployment may serve less.
| # | Model | Quality index | Intelligence Index | AA-LCR | Context | $ in / out per 1M |
|---|---|---|---|---|---|---|
| 1 | minimax | No data | No data | No data | 4M | No data |
| 2 | openai | 99.4 | 58.9 | 73.7% | 1.1M | $5 / $30 |
| 3 | openai | 98.2 | 55.0 | 74.0% | 1.1M | $2.5 / $15 |
| 4 | openai | 97.6 | 54.8 | 74.3% | 1.1M | $5 / $30 |
| 5 | openai | 95.9 | 51.4 | 74.0% | 1.1M | $2.5 / $15 |
| 6 | openai | 95.3 | 51.2 | 74.0% | 1.1M | $1 / $6 |
| 7 | openai | 86.5 | 40.0 | 69.3% | 1.1M | $0.75 / $4.5 |
| 8 | openai | 84.1 | 38.2 | 66.0% | 1.1M | $0.2 / $1.25 |
| 9 | openai | No data | No data | No data | 1.1M | $30 / $180 |
| 10 | openai | No data | No data | No data | 1.1M | $5 / $30 |
marks models served through the Inferbase API. Missing values are shown as No data rather than estimated.
Also worth evaluating
The next ranks after the top ten, for teams that want a wider shortlist.
Or stop choosing manually
This page exists because model choice is a per-task decision, and the honest answer changes as benchmarks and prices move. Routing makes that decision per request instead.
Send model="auto" to one OpenAI-compatible endpoint and each request is served by the best fit from the same quality and price data behind this ranking, with the decision disclosed per request.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/api/v1/inference",
api_key="YOUR_INFERBASE_KEY",
)
# Let routing pick the best model per request,
# or pin any model id from GET /models.
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "..."}],
)Frequently asked questions
How this ranking works and how to act on it.
No. Long-context recall degrades non-uniformly on most models, long prompts multiply per-request cost, and latency grows with input length. A model with a smaller window plus retrieval over your documents frequently beats stuffing everything into a giant window.
Because the figure is the creator’s published maximum, and serving deployments sometimes provision less. Inferbase tracks both the advertised maximum for display and the per-route servable context for actual routing decisions, and this page is explicit about showing the former.
Input tokens times the input price: a 200K-token prompt on a model priced at $3 per million input tokens costs about $0.60 before any output. The price column here makes that arithmetic immediate for each model.
Whole-codebase reasoning, long legal or technical documents where cross-references matter, and multi-document synthesis that retrieval fragments poorly. For needle-in-haystack lookups, retrieval remains cheaper and usually more accurate.
Yes. API keys accept a minimum context window constraint, and routing considers each candidate’s servable context, so requests are never sent to a model that cannot hold them.
Related rankings
The same evidence-first method, applied to other tasks.
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