Agnes AI: Models Intelligence, Performance & Price

Agnes AI
Agnes AI

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

Most Intelligent

#1
Agnes 2.5 Pro BetaAgnes 2.5 Pro Beta
49
#2
Agnes 2.5 Pro AlphaAgnes 2.5 Pro Alpha
40

Intelligence index

Total 2 models

Fastest

#1
Agnes 2.5 Pro AlphaAgnes 2.5 Pro Alpha
208 t/s
#2
Agnes 2.5 Pro BetaAgnes 2.5 Pro Beta
168 t/s

Output speed

Total 2 models

Lowest Price

#1
Agnes 2.5 Pro BetaAgnes 2.5 Pro Beta
$0.06
#2
Agnes 2.5 Pro AlphaAgnes 2.5 Pro Alpha
$0.19

Blended price (per 1M tokens)

Total 2 models

Agnes AI offers 2 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 Agnes AI are Agnes 2.5 Pro Beta (49) and Agnes 2.5 Pro Alpha (40).
  • For output speed, the fastest models are Agnes 2.5 Pro Alpha (208 t/s) and Agnes 2.5 Pro Beta (168 t/s).
  • For latency, Agnes 2.5 Pro Alpha (12.46s) and Agnes 2.5 Pro Beta (13.91s) offer the lowest time to first answer token.
  • For pricing, Agnes 2.5 Pro Beta ($0.06) and Agnes 2.5 Pro Alpha ($0.19) offer the lowest blended prices per 1M tokens.
  • For context window size, Agnes 2.5 Pro Beta (1M) and Agnes 2.5 Pro Alpha (1M) support the largest context windows on Agnes AI.
  • Agnes 2.5 Pro Beta provides the best balance of intelligence and cost-effectiveness. For the fastest output, Agnes 2.5 Pro Alpha is the top choice.

Highlights

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 v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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
See more
GDPval-AA v2

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

𝜏³-Banking

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

No data available
AutomationBench-AA

Agentic SaaS workflows

No data available
Harvey LAB-AA

Legal agentic work, criterion pass rate

No data available
EnterpriseOps-Gym-AA

Agentic business operations

No data available
AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

No data available
IFBench

Instruction following

No data available
APEX-Agents-AA

Long-horizon agentic tasks

No data available
ITBench-AA

Kubernetes incident root-cause analysis

No data available
MMMU-Pro

Visual reasoning

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

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Intelligence Index vs. Price

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

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

Context Window

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

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
Sapiens AI logo
Agnes 2.5 Pro Beta
1M
Proprietary
49
$0.03
168
2.02
16.88
11.89
Model
Provider
Sapiens AI logo
Agnes 2.5 Pro Alpha
1M
Open
40
--
208
2.86
14.86
9.60
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 Agnes AI

Agnes AI offers 2 models that we track: Agnes 2.5 Pro Beta and Agnes 2.5 Pro Alpha.

The most intelligent model available on Agnes AI is Agnes 2.5 Pro Beta with an Intelligence Index score of 49.

The fastest model on Agnes AI by output speed is Agnes 2.5 Pro Alpha at 208.3 tokens per second.

The model with the lowest time to first answer token on Agnes AI is Agnes 2.5 Pro Alpha at 12.46s. Lower latency means faster initial response time.

The most affordable model on Agnes AI by blended price is Agnes 2.5 Pro Beta at $0.06 per 1M tokens (7:2:1 cache hit/input/output ratio).

Prices on Agnes AI vary up to 3x across models, from $0.06 per 1M tokens for Agnes 2.5 Pro Beta to $0.19 per 1M tokens for Agnes 2.5 Pro Alpha.

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

Yes, all 2 models on Agnes AI support JSON mode for structured output.

Yes, all 2 models on Agnes AI support function calling (tool use).

Yes, Agnes AI offers 2 reasoning models: Agnes 2.5 Pro Beta and Agnes 2.5 Pro Alpha. Reasoning models use extended thinking to work through complex problems before providing an answer.

Yes, 1 of 2 models on Agnes AI are open weight models: Agnes 2.5 Pro Alpha.

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 Agnes AI, 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.