FriendliAI: Models Intelligence, Performance & Price

FriendliAI
FriendliAI

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

Most Intelligent

#1
GLM-5.3-FlashGLM-5.3-Flash
57
#2
GLM-5.2 (max)GLM-5.2 (max)
53
#3
GLM-5.1GLM-5.1
41
#4
GLM-5.1 (Non-reasoning)GLM-5.1 (Non-reasoning)
36
#5
MiniMax-M2.5MiniMax-M2.5
34

Intelligence index

Total 8 models

Fastest

#1
GLM-5.2 (max)GLM-5.2 (max)
181 t/s
#2
MiniMax-M2.5MiniMax-M2.5
173 t/s
#3
GLM-5.3-FlashGLM-5.3-Flash
172 t/s
#4
Gemma 4 31BGemma 4 31B
160 t/s
#5
Gemma 4 31B (Non-reasoning)Gemma 4 31B (Non-reasoning)
150 t/s

Output speed

Total 8 models

Lowest Price

#1
GLM-5.3-FlashGLM-5.3-Flash
$0.10
#2
Gemma 4 31BGemma 4 31B
$0.17
#3
Gemma 4 31B (Non-reasoning)Gemma 4 31B (Non-reasoning)
$0.17
#4
MiniMax-M2.5MiniMax-M2.5
$0.22
#5
DeepSeek V3.2 (Non-reasoning)DeepSeek V3.2 (Non-reasoning)
$0.42

Blended price (per 1M tokens)

Total 8 models

FriendliAI offers 8 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 FriendliAI are GLM-5.3-Flash (57), GLM-5.2 (max) (53), and GLM-5.1 (41).
  • For output speed, the fastest models are GLM-5.2 (max) (181 t/s), MiniMax-M2.5 (173 t/s), and GLM-5.3-Flash (172 t/s).
  • For latency, GLM-5.1 (Non-reasoning) (0.81s), DeepSeek V3.2 (Non-reasoning) (1.24s), and Gemma 4 31B (Non-reasoning) (1.38s) offer the lowest time to first answer token.
  • For pricing, GLM-5.3-Flash ($0.10), Gemma 4 31B ($0.17), and Gemma 4 31B (Non-reasoning) ($0.17) offer the lowest blended prices per 1M tokens. Prices vary up to 4.2x across models.
  • For context window size, GLM-5.3-Flash (1M), GLM-5.2 (max) (1M), and Gemma 4 31B (Non-reasoning) (262k) support the largest context windows on FriendliAI.
  • GLM-5.3-Flash provides the best balance of intelligence and cost-effectiveness. For the fastest output, GLM-5.2 (max) is the top choice.

Highlights

Artificial Analysis Intelligence Index · Higher is better
Output tokens per second · Higher is better
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
Estimate (independent evaluation forthcoming)

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

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

Agentic tool use

Agentic coding & terminal use

Coding

Reasoning & knowledge

Scientific reasoning

Physics reasoning

Long context reasoning

Agentic knowledge work, Elo

Agentic SaaS workflows

Legal agentic work, criterion pass rate

Agentic business operations

Quantitative analysis on spreadsheets & documents

No data available

Instruction following

Long-horizon agentic tasks

Kubernetes incident root-cause analysis

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
Z AI logo
GLM-5.3-Flash
1M
Open
57
$0.10
172
1.36
15.92
11.65
Z AI logo
GLM-5.2 (max)
1M
Open
53
$0.42
181
0.80
14.64
11.07
Z AI logo
GLM-5.1
203k
Open
41
$0.25
113
0.82
38.92
33.66
Z AI logo
GLM-5.1 (Non-reasoning)
203k
Open
36*
--
107
0.81
5.50
--
MiniMax logo
MiniMax-M2.5
197k
Open
34*
--
173
0.66
15.13
11.58
MiniMax logo
MiniMax-M2.1
197k
Open
32*
--
--
--
--
--
Google logo
Gemma 4 31B
256k
Open
30
$0.04
160
1.67
15.68
10.87
DeepSeek logo
DeepSeek V3.2 (Non-reasoning)
164k
Open
25*
--
65
1.24
8.97
--
Google logo
Gemma 4 31B (Non-reasoning)
262k
Open
22
$0.04
150
1.38
4.71
--

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 FriendliAI

The most intelligent model available on FriendliAI is GLM-5.3-Flash with an Intelligence Index score of 57.

The fastest model on FriendliAI by output speed is GLM-5.2 (max) at 180.7 tokens per second.

The model with the lowest time to first answer token on FriendliAI is GLM-5.1 (Non-reasoning) at 0.81s. Lower latency means faster initial response time.

The most affordable model on FriendliAI by blended price is GLM-5.3-Flash at $0.10 per 1M tokens (7:2:1 cache hit/input/output ratio).

Prices on FriendliAI vary up to 9x across models, from $0.10 per 1M tokens for GLM-5.3-Flash to $0.90 per 1M tokens for GLM-5.1 (Non-reasoning).

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

Yes, all 8 models on FriendliAI support JSON mode for structured output.

Yes, all 8 models on FriendliAI support function calling (tool use).

Yes, FriendliAI offers 5 reasoning models: GLM-5.3-Flash, GLM-5.2 (max), GLM-5.1, MiniMax-M2.5, and Gemma 4 31B. Reasoning models use extended thinking to work through complex problems before providing an answer.

Yes, all 8 models on FriendliAI 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 FriendliAI, 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.