Parasail: Models Intelligence, Performance & Price

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Parasail

This analysis is intended to support you in choosing the best model provided by Parasail for your use-case.

Most Intelligent

Updated

GLM-5.3 (max)

Kimi K3 (max)

GLM-5.3-Flash

DeepSeek V4.1 Flash (max)

DeepSeek V4 Flash 0731 (max)

Intelligence index

Total 29 models

Fastest

Qwen3 Next 80B A3B

187 t/s

GLM-5.2 (max) (NVFP4)

178 t/s

gpt-oss-120b (high)

162 t/s

gpt-oss-120b (low)

148 t/s

GLM-5.3 (max)

148 t/s

Output speed

Total 29 models

Lowest Price

Gemma 3 4B (FP8)

$0.05

Gemma 3 27B

$0.09

GLM-5.3-Flash

$0.10

Gemma 4 26B A4B

$0.10

Gemma 4 26B A4B (Non-reasoning)

$0.10

Blended price (per 1M tokens)

Total 29 models

Parasail offers 29 models, each with different intelligence, performance, and pricing characteristics. Below is a comparison of the key metrics across models.

For intelligence, the top models on Parasail are GLM-5.3 (max) (45), Kimi K3 (max) (44), and GLM-5.3-Flash (42).
For output speed, the fastest models are Qwen3 Next 80B A3B (187 t/s), GLM-5.2 (max) (NVFP4) (178 t/s), and gpt-oss-120b (high) (162 t/s).
For latency, Qwen3 Next 80B A3B (0.87s), Qwen3.6 35B A3B (Non-reasoning) (FP8) (0.92s), and Llama 4 Maverick (FP8) (0.95s) offer the lowest time to first answer token.
For pricing, Gemma 3 4B (FP8) ($0.05), Gemma 3 27B ($0.09), and GLM-5.3-Flash ($0.10) offer the lowest blended prices per 1M tokens. Prices vary up to 2.2x across models.
For context window size, Kimi K3 (max) (1M), DeepSeek V4.1 Flash (max) (1M), and DeepSeek V4 Flash 0731 (max) (1M) support the largest context windows on Parasail.

Highlights

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Price

USD per 1M tokens (blended) · Lower is better

Intelligence Evaluations

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

𝜏³-Banking

Agentic tool use

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AANew

Medical long context reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Intelligence Index vs. Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Pricing

Intelligence Index vs. Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Performance Summary

Output Speed vs. Price

Output speed: output tokens per second · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time vs. Price

End-to-end response time: end-to-end seconds to output 500 tokens · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).


									Further Analysis
GLM-5.3 (max)	1M	Open	45	$1.97	148	1.00	17.94	13.55	Model Provider
Kimi K3 (max)	1.05M	Open	44	$2.42	76	1.36	34.27	26.33	Model Provider
GLM-5.3-Flash	1M	Open	42	$0.28	100	1.03	26.07	20.03	Model Provider
DeepSeek V4.1 Flash (max)	1.05M	Open	39	$0.90	110	1.33	23.99	18.12	Model Provider
DeepSeek V4 Flash 0731 (max)	1.05M	Open	34	$0.41	69	1.06	37.43	29.10	Model Provider
GLM-5.2 (max) (NVFP4)	1M	Open	34	--	178	1.18	15.19	11.21	Model Provider
Qwen3.8 27B (xhigh) (FP8)	262k	Open	34	$2.29	67	1.38	38.69	29.85	Model Provider
MiniMax-M3 (MXFP8)	1M	Open	29	$0.46	139	1.29	19.22	14.34	Model Provider
Kimi K2.6	262k	Open	27	$0.50	84	1.30	60.17	52.93	Model Provider
DeepSeek V4 Flash (high) (FP8)	1.05M	Open	26*	--	65	1.60	28.27	19.01	Model Provider
DeepSeek V4 Flash (max) (FP8)	1.05M	Open	24	--	64	1.69	97.80	88.25	Model Provider
Kimi K2.6 (Non-reasoning) (INT4)	262k	Open	24*	--	80	1.39	7.64	--	Model Provider
Gemma 4 31B	262k	Open	19*	--	80	1.60	29.58	21.72	Model Provider
Qwen3.5 397B A17B	262k	Open	18	$0.30	49	1.01	75.81	64.66	Model Provider
Qwen3.6 35B A3B	262k	Open	18	$0.12	87	0.84	68.49	61.91	Model Provider
Gemma 4 26B A4B	256k	Open	17*	--	45	2.36	57.93	44.46	Model Provider
Qwen3.6 35B A3B (Non-reasoning) (FP8)	262k	Open	15*	--	92	0.92	6.34	--	Model Provider
Gemma 4 31B (Non-reasoning)	262k	Open	14*	--	75	1.52	8.19	--	Model Provider
Gemma 4 26B A4B (Non-reasoning)	262k	Open	13*	--	26	1.87	21.26	--	Model Provider
Qwen3 235B 2507	262k	Open	12*	--	36	1.10	14.86	--	Model Provider
gpt-oss-120b (high)	131k	Open	12	$0.06	162	0.95	16.39	12.35	Model Provider
gpt-oss-120b (low)	131k	Open	10*	--	148	0.89	17.79	13.52	Model Provider
Llama 4 Maverick (FP8)	1.05M	Open	10*	--	56	0.95	9.92	--	Model Provider
Qwen3 VL 235B A22B (FP8)	131k	Open	10*	--	32	1.44	16.98	--	Model Provider
Qwen3 Next 80B A3B	262k	Open	10*	--	187	0.87	3.55	--	Model Provider
Qwen3 Coder Next (FP8)	262k	Open	9	$0.12	73	1.15	7.96	--	Model Provider
Llama 3.3 70B (FP8)	131k	Open	8*	--	78	2.57	8.97	--	Model Provider
Olmo 3.1 32B Think	65.5k	Open	7*	--	--	--	--	--	Model Provider
Olmo 3 7B	65.5k	Open	5*	--	--	--	--	--	Model Provider
Gemma 3 27B	131k	Open	5	$0.10	40	2.18	14.60	--	Model Provider
Gemma 3 4B (FP8)	131k	Open	5*	--	80	1.30	7.52	--	Model Provider

Key definitions

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Frequently Asked Questions

Common questions about Parasail

The most intelligent model available on Parasail is GLM-5.3 (max) with an Intelligence Index score of 45.

The fastest model on Parasail by output speed is Qwen3 Next 80B A3B at 187.1 tokens per second.

The model with the lowest time to first answer token on Parasail is Qwen3 Next 80B A3B at 0.87s. Lower latency means faster initial response time.

The most affordable model on Parasail by blended price is Gemma 3 4B (FP8) at $0.05 per 1M tokens (7:2:1 cache hit/input/output ratio).

Prices on Parasail vary up to 51x across models, from $0.05 per 1M tokens for Gemma 3 4B (FP8) to $2.31 per 1M tokens for Kimi K3 (max).

Yes, Parasail offers an OpenAI-compatible API, making it easy to switch from OpenAI or use existing OpenAI SDK integrations.

Yes, all 29 models on Parasail support JSON mode for structured output.

28 of 29 models on Parasail support function calling (tool use).

Yes, Parasail offers 17 reasoning models: GLM-5.3 (max), Kimi K3 (max), GLM-5.3-Flash, DeepSeek V4.1 Flash (max), DeepSeek V4 Flash 0731 (max), GLM-5.2 (max) (NVFP4), Qwen3.8 27B (xhigh) (FP8), MiniMax-M3 (MXFP8), Kimi K2.6, DeepSeek V4 Flash (high) (FP8), DeepSeek V4 Flash (max) (FP8), Gemma 4 31B, Qwen3.5 397B A17B, Qwen3.6 35B A3B, Gemma 4 26B A4B, gpt-oss-120b (high), and gpt-oss-120b (low). Reasoning models use extended thinking to work through complex problems before providing an answer.

Yes, all 29 models on Parasail are open weight models.

Yes, provider performance can vary over time due to infrastructure changes, load balancing, and updates. We continuously benchmark all providers and display historical performance trends in the "Over Time" charts.

When choosing a model on Parasail, consider: intelligence (for quality-sensitive tasks), output speed (for throughput-intensive tasks), latency (for interactive applications requiring quick first responses), pricing (for cost-sensitive workloads), and features like context window size, JSON mode, or function calling support.

Parasail: Models Intelligence, Performance & Price

Most Intelligent

Fastest

Lowest Price

Intelligence