Microsoft Azure: Models Quality, Performance & Price
Analysis of Microsoft Azure's models across key metrics including quality, price, output speed, latency, context window & more. This analysis is intended to support you in choosing the best model provided by Microsoft Azure for your use-case. For more details including relating to our methodology, see our FAQs. Models analyzed: o1, o1-preview, o1-mini, GPT-4o (Aug '24), GPT-4o (May '24), GPT-4o (Nov '24), GPT-4o mini, Llama 3.3 70B, Llama 3.1 405B, Llama 3.1 70B, Llama 3.1 8B, Mistral Large 2 (Nov '24), Mistral Large 2 (Jul '24), Command-R+ (Apr '24), Command-R (Mar '24), Phi-3 Medium 14B, Jamba 1.5 Large, Jamba 1.5 Mini, GPT-4 Turbo, Llama 3 8B, Mistral Small (Feb '24), Mistral Large (Feb '24), and Jamba Instruct.
Link:
Azure Model Comparison Summary
Quality:
Mistral Large (Feb '24) and
Llama 3 8B are the highest quality models offered by Azure, followed by
Command-R+ (Apr '24),
Command-R (Mar '24) &
Jamba Instruct.Output Speed (tokens/s):
GPT-4o mini (172 t/s) and
Llama 3.1 8B (162 t/s) are the fastest models offered by Azure, followed by
GPT-4o (Nov '24),
GPT-4o (May '24) &
o1-mini.Latency (seconds):
Llama 3.1 8B (0.30s) and
Mistral Small (Feb '24) (0.37s) are the lowest latency models offered by Azure, followed by
Llama 3 8B,
Phi-3 Medium 14B &
Llama 3.1 70B.Blended Price ($/M tokens):
Jamba 1.5 Mini ($0.25) and
GPT-4o mini ($0.26) are the cheapest models offered by Azure, followed by
Phi-3 Medium 14B,
Llama 3.1 8B &
Llama 3 8B.Context Window Size:
Jamba 1.5 Large (256k) and
Jamba 1.5 Mini (256k) are the largest context window models offered by Azure, followed by
Jamba Instruct,
o1 &
o1-preview.









Highlights
Quality
Artificial Analysis Quality Index; Higher is better
Speed
Output Tokens per Second; Higher is better
Price
USD per 1M Tokens; Lower is better
Parallel Queries:
Prompt Length:
Features | Model Quality | Price | Output tokens/s | Latency | |||
---|---|---|---|---|---|---|---|
Further Analysis | |||||||
![]() | o1 | 200k | 90 | $26.25 | 39.7 | 30.70 | |
![]() | o1-preview | 128k | 86 | $28.88 | 46.9 | 28.91 | |
![]() | o1-mini | 128k | 84 | $5.78 | 125.6 | 14.00 | |
![]() | GPT-4o (Aug '24) | 128k | 78 | $4.38 | 107.4 | 0.81 | |
![]() | GPT-4o (May '24) | 128k | 78 | $7.50 | 128.1 | 0.81 | |
![]() | GPT-4o (Nov '24) | 128k | 75 | $4.38 | 137.5 | 0.87 | |
![]() | GPT-4o mini | 128k | 73 | $0.26 | 171.7 | 0.79 | |
![]() | Llama 3.3 70B | 128k | 74 | $0.71 | 52.7 | 0.45 | |
![]() | Llama 3.1 405B | 128k | 74 | $8.00 | 21.4 | 0.53 | |
![]() | Llama 3.1 70B | 128k | 67 | $2.90 | 60.5 | 0.43 | |
![]() | Llama 3.1 8B | 128k | 54 | $0.38 | 162.1 | 0.30 | |
![]() | ![]() Mistral Large 2 (Nov '24) | 128k | 74 | $3.00 | 35.0 | 0.52 | |
![]() | ![]() Mistral Large 2 (Jul '24) | 128k | 74 | $3.00 | 35.4 | 0.53 | |
![]() | ![]() Command-R+ (Apr '24) | 128k | 44 | $6.00 | 49.1 | 0.58 | |
![]() | ![]() Command-R (Mar '24) | 128k | 36 | $0.75 | 79.2 | 0.45 | |
![]() | Phi-3 Medium 14B | 128k | $0.30 | 49.9 | 0.43 | ||
![]() | ![]() Jamba 1.5 Large | 256k | 64 | $3.50 | 50.8 | 0.69 | |
![]() | ![]() Jamba 1.5 Mini | 256k | $0.25 | 82.1 | 0.48 | ||
![]() | GPT-4 Turbo | 128k | 75 | $15.00 | 45.4 | 1.56 | |
![]() | Llama 3 8B | 8k | 45 | $0.38 | 73.9 | 0.37 | |
![]() | ![]() Mistral Small (Feb '24) | 33k | 59 | $1.50 | 53.6 | 0.37 | |
![]() | ![]() Mistral Large (Feb '24) | 33k | 55 | $6.00 | 39.4 | 0.50 | |
![]() | ![]() Jamba Instruct | 256k | 28 | $0.55 | 74.8 | 0.52 |
Key definitions
Artificial Analysis Quality Index: Average result across our evaluations covering different dimensions of model intelligence. Currently includes MMLU, GPQA, Math & HumanEval. OpenAI o1 model figures are preliminary and are based on figures stated by OpenAI. See methodology for more details.
Context window: Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).
Output Speed: 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).
Latency: Time to first token of tokens received, in seconds, after API request sent. For models which do not support streaming, this represents time to receive the completion.
Price: Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).
Output Price: Price per token generated by the model (received from the API), represented as USD per million Tokens.
Input Price: Price per token included in the request/message sent to the API, represented as USD per million Tokens.
Time period: Metrics are 'live' and are based on the past 14 days of measurements, measurements are taken 8 times a day for single requests and 2 times per day for parallel requests.