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Model comparison
DeepSeek's DeepSeek V4.1 Flash 1017 against Meta's Muse Spark 1.2: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
DeepSeek V4.1 Flash 1017 lists at $0.262 per million tokens blended against $2.00 for Muse Spark 1.2 - 7.6x less.
Intelligence index 40 against 40: within a point of each other.
only DeepSeek V4.1 Flash 1017 offers open weights.
DeepSeek V4.1 Flash 1017 was released about 2 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | DeepSeek V4.1 Flash 1017 | Muse Spark 1.2 |
|---|---|---|
| Input price / 1M tokens | $0.15 | $1.25 |
| Output price / 1M tokens | $0.60 | $4.25 |
| Blended (3 in : 1 out) | $0.26 | $2.00 |
| Cached input / 1M | $0.003 | $0.15 |
| Context window | 1.0M | 1.0M |
| Max output | 393K | 944K |
| Input | text, image | text, image, video, file |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 40 | 40 |
| Coding index | - | 72 |
| Agentic index | - | 43 |
| Released | Oct 17, 2026 | Aug 5, 2026 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | DeepSeek V4.1 Flash 1017 | Muse Spark 1.2 | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $6.00 | $46.25 | $40.25 less on DeepSeek V4.1 Flash 1017 |
| Document summaries100M in, 5M out - long inputs, short answers. | $18.00 | $146 | $128 less on DeepSeek V4.1 Flash 1017 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $21.00 | $160 | $139 less on DeepSeek V4.1 Flash 1017 |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: DeepSeek V4.1 Flash 1017 wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: deepseek-v4.1-flash-1017, kind: llm, metadata: { model: deepseek/deepseek-v4.1-flash-1017 } }
- { id: muse-spark-1.2, kind: llm, metadata: { model: meta/muse-spark-1.2 } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [deepseek-v4.1-flash-1017]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [muse-spark-1.2]
weight: 0.8Every DeepSeek and Meta model, with prices, is on the vendor pages; the rankings show what the router actually picks.