leaderboard

30 of 807 by context
Top 30 models ranked by context
#modelserved bycontextshare of leadermax out
01grok-4.2-betaxAI2M-
02grok-4-1-fast-non-reasoningxAI2M-
03grok-4-1-fast-reasoningxAI2M-
04grok-4-fastxAI2M-
05grok-4-fast-non-reasoningxAI2M-
06gpt-6.1-terra:flexOpenAI1.1M128K
07gpt-6.1-terra @eastusMicrosoft Azure AI1.1M128K
08gpt-6.1-terra @francecentralMicrosoft Azure AI1.1M128K
09gpt-6.1-terra @germanywestcentralMicrosoft Azure AI1.1M128K
10gpt-6.1-terra @swedencentralMicrosoft Azure AI1.1M128K
11gpt-6.1-terra @westeuropeMicrosoft Azure AI1.1M128K
12gpt-6.1-terraOpenAI1.1M128K
13gpt-6.1-solOpenAI1.1M128K
14gpt-6.1-sol:flexOpenAI1.1M128K
15gpt-6.1-sol @eastus2Microsoft Azure AI1.1M128K
16gpt-6.1-sol @francecentralMicrosoft Azure AI1.1M128K
17gpt-6.1-sol @germanywestcentralMicrosoft Azure AI1.1M128K
18gpt-6.1-sol @swedencentralMicrosoft Azure AI1.1M128K
19gpt-6.1-sol @westeuropeMicrosoft Azure AI1.1M128K
20gpt-6-luna @eastus2Microsoft Azure AI1.1M128K
21gpt-6-sol @eastus2Microsoft Azure AI1.1M128K
22gpt-6-luna @francecentralMicrosoft Azure AI1.1M128K
23gpt-6-luna @germanywestcentralMicrosoft Azure AI1.1M128K
24gpt-6-luna @swedencentralMicrosoft Azure AI1.1M128K
25gpt-6-luna @westeuropeMicrosoft Azure AI1.1M128K
26gpt-6-sol @francecentralMicrosoft Azure AI1.1M128K
27gpt-6-sol @germanywestcentralMicrosoft Azure AI1.1M128K
28gpt-6-sol @swedencentralMicrosoft Azure AI1.1M128K
29gpt-6-sol @westeuropeMicrosoft Azure AI1.1M128K
30gpt-6-luna:flexOpenAI1.1M128K

Bars are each context window's share of the largest in this list.

top of list

longest-context
context2Mgrok-4.2-beta
entries80730 shown
served byxAIhigh first

grok-4.2-beta and grok-4-1-fast-non-reasoning are effectively tied at the top of this list

distribution

30 entries
med 1.1M2M#1#30

One bar per ranked model, best on the left, drawn as a share of the leader. 5 models sit within 10 percent of the leader, so the top of this list is a cluster rather than a winner. The dashed line is the median.

notes

reference

Longest context 30 ranked by context

A larger context window means more tokens you can fit in a single prompt, useful for whole-codebase analysis, long document Q&A, and agentic workflows. Note: effective quality often degrades past 128K tokens; prompt caching (supported on many models) is usually a better approach for repeated long context than brute-forcing more tokens in every call.

method

Ranked by maximum context window, the total tokens (input plus output) a model accepts in one request. Effective quality often degrades well below the advertised maximum, and most production workloads do better with prompt caching and retrieval than with more tokens per call.

Built from the same catalog the router reads at request time, rebuilt daily.

one api for every model on this list

Requesty is OpenAI-compatible. Switch between any two models above by changing one parameter, and keep the failover, caching and spend controls.

807 entriesshowing 30sort high firstsource contextleader grok-4.2-beta 2Mrebuilt daily