Context Window Efficiency Explorer

Analyzes score-per-context-token ratio across 300 AI models to find those that make the best use of their context window, output capacity, and cost.

Context Window vs Score

LMMarketCap.com

Efficiency Overview

Key efficiency metrics across all analyzed models.

Most Efficient (128K+)

GPT-5.3 Chat

707.0 score/MToken

Best Output Efficiency

Gemma 2 27B

37.8 score/1K output

Best Cost Efficiency

Ling-2.6-flash

2000.0 score/$

Avg Overall Efficiency

4.4%

normalized across all models

Efficiency Rankings

Top 50 models ranked by score per million context tokens.

#	Model	Provider	Score	Context	Output	Score/MToken	Output Eff.	Cost Eff.	Tier
1	Gemma 2 27BGoogle	Google	77	8K	2K	9448.2	37.8	119.1	Small
2	GPT-4OpenAI	OpenAI	65	8K	4K	7911.1	15.8	1.4	Small
3	R1 Distill Llama 70BDeepSeek	DeepSeek	41	8K	8K	4968.3	5.0	50.9	Small
4	Phi 4Microsoft	Microsoft	60	16K	16K	3674.3	3.7	573.3	Small
5	Reka Edgerekaai	rekaai	40	16K	16K	2441.4	2.4	400.0	Small
6	Perceptron Mk1perceptron	perceptron	40	33K	8K	1220.7	4.9	48.5	Medium
7	MiniMax M2-herMiniMax	MiniMax	69	66K	2K	1054.4	33.7	92.1	Medium
8	Mixtral 8x22B InstructMistral AI	Mistral AI	63	66K	--	967.4	--	15.8	Medium
9	GLM 4.5VZhipu AI	Zhipu AI	63	66K	16K	953.7	3.8	52.1	Medium
10	Olmo 3 32B ThinkAllen AI	Allen AI	55	66K	66K	837.7	0.8	168.9	Medium
11	Qwen3 30B A3B Thinking 2507Alibaba	Alibaba	64	82K	33K	782.5	2.0	49.3	Medium
12	GPT-5.3 ChatOpenAI	OpenAI	91	128K	16K	707.0	5.5	11.5	Large
13	GPT-5.2 ChatOpenAI	OpenAI	91	128K	16K	707.0	5.5	11.5	Large
14	GLM 4.5Zhipu AI	Zhipu AI	75	131K	98K	573.0	0.8	53.6	Large
15	GPT-4o (batch)OpenAI	OpenAI	72	128K	16K	562.5	4.4	23.0	Large
16	GPT-4o (2024-11-20)OpenAI	OpenAI	71	128K	16K	556.3	4.3	11.4	Large
17	GPT-4o (2024-08-06)OpenAI	OpenAI	71	128K	16K	556.3	4.3	11.4	Large
18	GPT-4oOpenAI	OpenAI	71	128K	16K	556.3	4.3	11.4	Large
19	GPT-4o (2024-05-13)OpenAI	OpenAI	71	128K	4K	556.3	17.4	7.1	Large
20	GPT-4o-miniOpenAI	OpenAI	69	128K	16K	541.4	4.2	184.8	Large
21	GLM 4.5 AirZhipu AI	Zhipu AI	71	131K	98K	539.4	0.7	144.3	Large
22	Qwen3 VL 235B A22B ThinkingAlibaba	Alibaba	69	131K	33K	529.5	2.1	31.5	Large
23	GPT-4 TurboOpenAI	OpenAI	67	128K	4K	521.1	16.3	3.3	Large
24	Mistral LargeMistral AI	Mistral AI	66	128K	--	514.8	--	16.5	Large
25	Llama 3.3 70B InstructMeta	Meta	67	131K	16K	509.6	4.1	318.1	Large
26	GPT-4 Turbo PreviewOpenAI	OpenAI	65	128K	4K	506.2	15.8	3.2	Large
27	Mistral Large 2407Mistral AI	Mistral AI	66	131K	--	502.8	--	16.5	Large
28	GLM 4.6VZhipu AI	Zhipu AI	66	131K	33K	499.7	2.0	109.2	Large
29	Llama 3.1 70B InstructMeta	Meta	65	131K	16K	498.2	4.0	163.3	Large
30	DeepSeek V3.2DeepSeek	DeepSeek	81	164K	164K	496.2	0.5	254.1	Large
31	Qwen3 30B A3BAlibaba	Alibaba	64	131K	16K	489.0	3.9	206.8	Large
32	GPT-4o-mini (batch)OpenAI	OpenAI	63	128K	16K	488.3	3.8	333.3	Large
33	R1 0528DeepSeek	DeepSeek	79	164K	33K	484.6	2.4	59.9	Large
34	GPT-4 Turbo (batch)OpenAI	OpenAI	62	128K	4K	480.5	15.0	6.2	Large
35	Mercury 2Inception	Inception	61	128K	50K	476.6	1.2	122.0	Large
36	Qwen3 8BAlibaba	Alibaba	61	131K	8K	465.4	7.4	213.3	Large
37	R1DeepSeek	DeepSeek	74	164K	16K	452.9	4.6	46.4	Large
38	GPT-4o-mini (2024-07-18)OpenAI	OpenAI	57	128K	16K	441.4	3.4	150.7	Large
39	DeepSeek V3.2 ExpDeepSeek	DeepSeek	72	164K	66K	438.2	1.1	211.2	Large
40	DeepSeek V3.1DeepSeek	DeepSeek	72	164K	33K	438.2	2.2	119.7	Large
41	DeepSeek V3 0324DeepSeek	DeepSeek	72	164K	66K	438.2	1.1	103.3	Large
42	gpt-oss-20bOpenAI	OpenAI	57	131K	131K	437.9	0.4	717.5	Large
43	gpt-oss-20b (free)OpenAI	OpenAI	57	131K	33K	437.9	1.8	Free	Large
44	o3 ProOpenAI	OpenAI	87	200K	100K	433.5	0.9	1.7	Large
45	o3 Pro (batch)OpenAI	OpenAI	87	200K	100K	433.5	0.9	3.5	Large
46	o3OpenAI	OpenAI	87	200K	100K	433.5	0.9	17.3	Large
47	o3 (batch)OpenAI	OpenAI	87	200K	100K	433.5	0.9	34.7	Large
48	Claude Opus 4.5Anthropic	Anthropic	85	200K	64K	425.5	1.3	5.7	Large
49	Claude Opus 4.5 (batch)Anthropic	Anthropic	85	200K	64K	425.5	1.3	11.3	Large
50	DeepSeek V3DeepSeek	DeepSeek	70	164K	16K	424.2	4.3	108.1	Large

Tier Analysis

Efficiency breakdown across context window tiers.

Small5 models

Avg Score57

Score/MToken5688.7

Best

Gemma 2 27B

Worst

Reka Edge

Medium6 models

Avg Score59

Score/MToken969.4

Best

Perceptron Mk1

Worst

Qwen3 30B A3B Thinking 2507

Large174 models

Avg Score65

Score/MToken311.8

Best

GPT-5.3 Chat

Worst

Grok Latest

Mega115 models

Avg Score73

Score/MToken73.1

Best

GLM 5.2 (batch)

Worst

OpenAI GPT Latest

Diminishing Returns Analysis

Are bigger context windows correlated with higher scores?

Tier	Avg Context	Avg Score	Avg Efficiency
Small	11K	57	5688.7
Medium	63K	59	969.4
Large	236K	65	311.8
Mega	1.0M	73	73.1

Output Token Efficiency

Top 20 models by output efficiency (score per 1K output tokens). Models with 16K+ output tokens are highlighted.

Model	Provider	Score	Max Output	Output Eff.
Gemma 2 27BGoogle	Google	77	2K	37.8
MiniMax M2-herMiniMax	MiniMax	69	2K	33.7
GPT-4o (2024-05-13)OpenAI	OpenAI	71	4K	17.4
GPT-4 TurboOpenAI	OpenAI	67	4K	16.3
GPT-4 Turbo PreviewOpenAI	OpenAI	65	4K	15.8
GPT-4OpenAI	OpenAI	65	4K	15.8
GPT-4 Turbo (batch)OpenAI	OpenAI	62	4K	15.0
Claude 3 HaikuAnthropic	Anthropic	51	4K	12.5
Command R (08-2024)Cohere	Cohere	49	4K	12.2
Command R+ (08-2024)Cohere	Cohere	49	4K	12.2
Qwen3 8BAlibaba	Alibaba	61	8K	7.4
Qwen3 235B A22BAlibaba	Alibaba	54	8K	6.6
Command ACohere	Cohere	51	8K	6.2
GPT-5.3 ChatOpenAI16K+	OpenAI	91	16K	5.5
GPT-5.2 ChatOpenAI16K+	OpenAI	91	16K	5.5
R1 Distill Llama 70BDeepSeek	DeepSeek	41	8K	5.0
Nemotron 3.5 Content Safety (free)NVIDIA	NVIDIA	40	8K	4.9
Perceptron Mk1perceptron	perceptron	40	8K	4.9
Palmyra X5Writer	Writer	40	8K	4.9
Falcon-H1-Arabic 34B InstructTII	TII	40	8K	4.9

Key Insights

Auto-generated observations from the efficiency data.

Context Sweet Spot

Small models have the highest average efficiency at 5688.7 score/MToken across 5 models.

Output Matters

Models with 16K+ output tokens score 29% higher on average than models with smaller output limits.

Compact High Performers

0 models achieve top-20 scores with under 128K context.

Explore More

Dive deeper into context windows, compare models, or explore other dimensions.

All Explorers Context Windows Large Context Models

Frequently Asked Questions

Efficiency is measured as the score-per-context-token ratio - how much ranking score a model achieves relative to its context window size. Models that score highly with smaller context windows are considered more efficient than those requiring massive context to achieve similar results.

Cost efficiency combines quality (composite score) with pricing. The most cost-efficient models achieve high benchmark scores while maintaining low per-token API costs. Free and budget-tier models that perform well are the most cost-efficient options.

Not necessarily. Our efficiency analysis shows diminishing returns beyond certain context sizes. Models with 128K tokens often score similarly to those with 1M+ tokens, meaning the extra context capacity adds cost without proportional quality gains for most use cases.