Beam 1.0 vs Qwen3.6-27B
Beam 1.0 leads the LLM Stats Score 44.6 to 35.2. Beam 1.0 is 1.7x cheaper per token.
Reflection AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 35.2, ranking #46 overall.
In the 6 individual benchmarks reported for both models, Beam 1.0 wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, Beam 1.0 is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Beam 1.0 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Beam 1.0
- overall performance matters — it scores 44.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 6 of 6 exact shared results
- cost matters — it's about 1.7x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3.6-27B
- you want predictable pricing at $0.32/M input and $3.20/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for Beam 1.0 · 44 for Qwen3.6-27B
Beam 1.0 outperforms in 6 benchmarks (AIME 2026, GPQA, Humanity's Last Exam, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified), while Qwen3.6-27B is better at 0 benchmarks.
Beam 1.0 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Beam 1.0 ($0.40/1M tokens) is 1.3x more expensive than Qwen3.6-27B ($0.32/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 2.7x cheaper than Qwen3.6-27B ($3.20/1M tokens).
In conclusion, Qwen3.6-27B is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 473.5B more parameters than Qwen3.6-27B, making it 1704.4% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Qwen3.6-27B's 262,144 tokens. Qwen3.6-27B can generate longer responses up to 262,144 tokens, while Beam 1.0 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.6-27B supports multimodal inputs, whereas Beam 1.0 does not.
Qwen3.6-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Qwen3.6-27B
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Qwen3.6-27B was released on 2026-04-21.
Beam 1.0 is 6 months newer than Qwen3.6-27B.
Oct 16, 2026
3 days ago
5mo newerApr 21, 2026
6 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Qwen3.6-27B's cutoff date is not specified.
We can confirm Beam 1.0's training data extends to 2026-06-30, but cannot make a direct comparison without Qwen3.6-27B's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Qwen3.6-27B is available from DeepInfra, Novita.
Beam 1.0
Qwen3.6-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Qwen3.6-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Qwen3.6-27B.