DeepSeek V4.1 Flash, Meta’s Muse Spark Open and Reflection’s Beam 1.0 each offer a one-million-token context window, but a new comparison says they suit quite different jobs. Its most useful finding is also its most sobering: there is no confirmed independent leaderboard putting all three through the same test.
DeepSeek Watch analysis
What happened
Shattered.io compares the models’ reported specifications, prices, licences and deployment options. It casts DeepSeek as the low-cost hosted multimodal option, Muse Spark Open as the text, image, video and audio heavyweight, and Beam as the Apache 2.0-licensed coding and agent model.
The article lists DeepSeek V4.1 Flash output at US$0.60 per million tokens off-peak, while Beam’s hosted OpenRouter pricing starts at US$0.25 per million input tokens and US$1 per million output tokens. It says all three offer a context window of about one million tokens, but warns that long-context performance and cost still need testing on the workload in question. Read Shattered.io’s comparison.
Why it matters
These are not interchangeable models with different logos on the box. The article’s comparison makes the trade-offs concrete: DeepSeek’s cited price may appeal to API users; Muse Spark Open’s listed modalities matter for teams working with audio or video; and Beam’s Apache 2.0 licence may be attractive where commercial reuse and modification matter.
But the comparison does not establish that one model is best overall. Shattered.io says it could not confirm an independent, shared-harness ranking for all three, and distinguishes vendor-reported figures from confirmed specifications. For anyone choosing a model, that makes the guide a useful shortlist, not a substitute for checking the relevant model card, licence and provider pricing.
Our read
A million-token window is a headline, not a guarantee that a model will retrieve the right detail from a million tokens. The sensible takeaway is to shortlist by task, cost and licence, then run the same prompts and settings on each contender. A leaderboard would be tidier; your own workload is more likely to pay the bills.
What to watch
- Whether an independent evaluation tests all three models on the same tasks and harness.
- Whether providers change pricing, model access or context limits.
- Whether the licence and deployment details hold up against the exact model versions teams plan to use.
Discussion spark: When choosing an open-weight model, should teams prioritise low API cost, broader modalities or a permissive licence, and how much should shared benchmarks matter before they commit?
Sources and evidence
- Muse Spark Open vs DeepSeek V4.1 vs Beam: 1M Context [2026] – shattered.io (6 November 2026, 05:18 UTC)
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