Qwen3.6 27B (Reasoning) logo
Alibaba
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Open weights model

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Released April 2026

Qwen3.6 27B (Reasoning) API Provider Benchmarking & Analysis

Model Comparison

Analysis of API providers for Qwen3.6 27B (Reasoning) across performance metrics including latency (time to first token), output speed (output tokens per second), price and others. API providers benchmarked include DeepInfra FP8, Groq, Novita, Alibaba Cloud, SiliconFlow (FP8).

Fastest

#1
GroqGroq
446.3 t/s
#2
Alibaba CloudAlibaba Cloud
55.7 t/s
#3
NovitaNovita
53.3 t/s
#4
DeepInfra FP8DeepInfra FP8
44.8 t/s
#5
SiliconFlow (FP8)SiliconFlow (FP8)
41.7 t/s

Output speed

Total 5 providers

Lowest Latency

#1
GroqGroq
13.76 s
#2
Alibaba CloudAlibaba Cloud
105.67 s
#3
NovitaNovita
109.39 s
#4
DeepInfra FP8DeepInfra FP8
128.58 s
#5
SiliconFlow (FP8)SiliconFlow (FP8)
139.53 s

Time to first answer token

Total 5 providers

Lowest Price

#1
SiliconFlow (FP8)SiliconFlow (FP8)
$0.59
#2
DeepInfra FP8DeepInfra FP8
$0.61
#3
GroqGroq
$0.84
#4
NovitaNovita
$0.90
#5
Alibaba CloudAlibaba Cloud
$0.90

Blended price (per 1M tokens)

Total 5 providers

Qwen3.6 27B is available through 5 API providers, each offering different performance characteristics and pricing. Below is a comparison of the key metrics across providers.

  • For output speed, the top providers are Groq (446.3 t/s), Alibaba Cloud (55.7 t/s), Novita (53.3 t/s). Speed varies significantly across providers, with a 970% difference between the fastest and slowest.
  • For latency, Groq (13.76s), Alibaba Cloud (105.67s), Novita (109.39s) offer the lowest time to first answer token.
  • For pricing, SiliconFlow (FP8) (0.59), DeepInfra FP8 (0.61), Groq (0.84) offer the lowest blended prices per 1M tokens.
  • Groq offers the best performance with both the highest speed and lowest latency. For cost optimization, SiliconFlow (FP8) provides the most competitive pricing.

Highlights

Speed

Output tokens per second · Higher is better

End-to-End Response Time

Seconds · Lower is better

Price

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

Update: Default performance benchmarking workload has updated to 10k input tokens to better reflect production use cases. You can still select different workloads above.

Pricing

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens) · Lower is better · 10,000 input tokens

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Pricing: Blended Price

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

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).

Output Speed vs. Price

Blended at 7:2:1 (cache-input-output) · 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).

Speed

Measured by Output Speed (tokens per second)

Output Speed: Qwen3.6 27B (Reasoning)

Output speed: output tokens per second · 10,000 input tokens

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 vs. Output Speed

Latency: seconds to first token received · Output speed: output tokens per second · 10,000 input tokens
Most attractive quadrant

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

Measured by Time (seconds) to First Token

Time to First Answer Token

Seconds to first token received · Lower is better · 10,000 input tokens

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

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · 10,000 input tokens

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Key Comparison Metrics & API Features

DeepInfra logoDeepInfra
262k
Open
$0.19
45
1.79
139.74
126.78
Groq logoGroq
131k
Open
$0.28
446
1.05
14.89
12.72
Novita logoNovita
262k
Open
$0.29
53
2.84
118.77
106.54
Alibaba Cloud logoAlibaba Cloud
262k
Open
$0.29
56
3.72
114.65
101.94
SiliconFlow logoSiliconFlow
262k
Open
$0.19
42
3.49
151.51
136.04

Frequently Asked Questions

Common questions about Qwen3.6 27B (Reasoning) providers

Qwen3.6 27B (Reasoning) is available through 5 API providers: DeepInfra FP8, Groq, Novita, Alibaba Cloud, and SiliconFlow (FP8). Each provider offers different performance characteristics and pricing.

Qwen3.6 27B (Reasoning) is currently available through 5 API providers that we benchmark and track.

The fastest providers for Qwen3.6 27B (Reasoning) by output speed are Groq (446.3 t/s), Alibaba Cloud (55.7 t/s), and Novita (53.3 t/s). Output speed measures how quickly tokens are generated after the model starts responding.

The providers with the lowest time to first answer token for Qwen3.6 27B (Reasoning) are Groq (13.76s), Alibaba Cloud (105.67s), and Novita (109.39s). Lower latency means faster initial response time.

The most affordable providers for Qwen3.6 27B (Reasoning) by blended price are SiliconFlow (FP8) ($0.59 per 1M tokens), DeepInfra FP8 ($0.61 per 1M tokens), and Groq ($0.84 per 1M tokens). Blended price uses a 7:2:1 cache hit/input/output token ratio.

The providers with the lowest input token pricing for Qwen3.6 27B (Reasoning) are SiliconFlow (FP8) ($0.30 per 1M input tokens), DeepInfra FP8 ($0.32 per 1M input tokens), and Groq ($0.60 per 1M input tokens).

The providers with the lowest output token pricing for Qwen3.6 27B (Reasoning) are Groq ($3.00 per 1M output tokens), DeepInfra FP8 ($3.20 per 1M output tokens), and SiliconFlow (FP8) ($3.20 per 1M output tokens).

Prices for Qwen3.6 27B (Reasoning) vary up to 1.5x across providers. The most affordable is SiliconFlow (FP8) at $0.59 per 1M tokens, while Novita charges $0.90 per 1M tokens.

Output speed for Qwen3.6 27B (Reasoning) varies significantly across providers. Groq is the fastest at 446.3 t/s, which is 10.7x faster than SiliconFlow (FP8) at 41.7 t/s.

All 5 providers of Qwen3.6 27B (Reasoning) support JSON mode for structured output.

All 5 providers of Qwen3.6 27B (Reasoning) support function calling (tool use).

For Qwen3.6 27B (Reasoning), Groq offers the best performance with highest speed and lowest latency. For cost optimization, SiliconFlow (FP8) provides the most competitive pricing.

When choosing a provider for Qwen3.6 27B (Reasoning), consider: output speed (for throughput-intensive tasks), latency (for interactive applications requiring quick first responses), pricing (for cost-sensitive workloads), and API features like JSON mode or function calling.

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.

For information about Qwen3.6 27B (Reasoning)'s intelligence, capabilities, modalities, and how it compares to other models, see the model overview page. View model overview