Beam 1.0 vs Kimi K2.6
Beam 1.0 and Kimi K2.6 are closely matched at 44.6 and 43.4 on the LLM Stats Score. Beam 1.0 is 2.4x cheaper per token.
Reflection AI · Moonshot AI · Updated for 2026
Which is better?
Beam 1.0 and Kimi K2.6 are closely matched on the overall LLM Stats Score at 44.6 and 43.4.
In the 9 individual benchmarks reported for both models, Beam 1.0 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Beam 1.0 is roughly 2.4x 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
- you value its reported benchmark strengths — it wins 5 of 9 exact shared results
- cost matters — it's about 2.4x 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 Kimi K2.6
- you want predictable pricing at $0.75/M input and $3.50/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 · 28 for Kimi K2.6
Beam 1.0 outperforms in 5 benchmarks (AIME 2026, GPQA, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified), while Kimi K2.6 is better at 4 benchmarks (BrowseComp, DeepSearchQA, Humanity's Last Exam, SciCode).
Beam 1.0 has a slight edge in benchmark performance.
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 1.9x cheaper than Kimi K2.6 ($0.75/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 2.9x cheaper than Kimi K2.6 ($3.50/1M tokens).
In conclusion, Kimi K2.6 is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.6 has 498.7B more parameters than Beam 1.0, making it 99.5% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Kimi K2.6's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.6 supports multimodal inputs, whereas Beam 1.0 does not.
Kimi K2.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Kimi K2.6
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Kimi K2.6 uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Kimi K2.6 was released on 2026-04-20.
Beam 1.0 is 6 months newer than Kimi K2.6.
Oct 16, 2026
3 days ago
5mo newerApr 20, 2026
6 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Kimi K2.6'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 Kimi K2.6's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Kimi K2.6 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
Beam 1.0
Kimi K2.6
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Kimi K2.6 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Kimi K2.6.