Muse Spark 1.1 vs DeepSeek-R1 vs Gemini 3.6 Flash
Too close to call on our weighted score (Gemini 3.6 Flash 73, Muse Spark 1.1 70, DeepSeek-R1 51). The right pick depends on what you value most.
MetaMuse Spark 1.1
70/100- ECI154.3
- Price$1.25 / $4.25
- Context1.05M
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
GoogleGemini 3.6 Flash
73/100- ECI154.3
- Price$0.75 / $3.75
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 3 points (Gemini 3.6 Flash 73/100, Muse Spark 1.1 70/100, DeepSeek-R1 51/100), so choose by what matters most for your work: Gemini 3.6 Flash for raw capability and DeepSeek-R1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 3.6 FlashCapabilities Index (ECI): Gemini 3.6 Flash 154.3 · Muse Spark 1.1 154.3 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Gemini 3.6 Flash $1.50 · Muse Spark 1.1 $2.00 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.1 and Gemini 3.6 FlashMuse Spark 1.1 1,048,576 · Gemini 3.6 Flash 1,048,576 · DeepSeek-R1 128,000 tokens
- Widest inputsGemini 3.6 FlashMuse Spark 1.1: Text, Images, PDFs, Video · DeepSeek-R1: Text · Gemini 3.6 Flash: Text, Images, PDFs, Audio, Video
- Self-hostingDeepSeek-R1Publishes downloadable weights
| Measure | Weight | Muse Spark 1.1 | DeepSeek-R1 | Gemini 3.6 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 64 | 84 |
| Price | 25% | 36 | 47 | 42 |
| Inputs & features | 15% | 90 | 35 | 100 |
| Context window | 10% | 61 | 24 | 61 |
| Overall | 100% | 70/100 | 51/100 | 73/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Muse Spark 1.1Meta | DeepSeek-R1DeepSeek | Gemini 3.6 FlashGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 154.3 | 139.0 | 154.3 (best) |
| ECI rank | #35 of 148 | #104 of 148 | #34 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | 71.7% | 94.1% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 59.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 53.3% | 94.2% (best) |
| SimpleQA VerifiedShort factual questions | 57.8% | — | 66.2% (best) |
| Price per million tokens | |||
| Input | $1.25 | $0.70 (best) | $0.75 |
| Output | $4.25 | $2.60 (best) | $3.75 |
| Cached input | $0.15 | — | $0.075 (best) |
| Blended (3:1) | $2.00 | $1.18 (best) | $1.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Meta API | Median of 11 providers | Official Google API |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 128,000 tokens | 1,048,576 tokens (best) |
| Max output | 131,072 tokens (best) | 32,768 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | Yes |
| Video | Yes | No | Yes |
| Reasoning | Yesminimal · low · medium · high · xhigh | Yes | Yesminimal · low · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | muse-spark-1.1 | — | gemini-3.6-flash |
| API providers | 13 | 12 | 25 (best) |
| Released | Jul 9, 2026 | Jan 20, 2025 | Jul 21, 2026 |
| Knowledge cutoff | — | Jul 2024 | Mar 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Muse Spark 1.1$21.00
- DeepSeek-R1$12.20
- Gemini 3.6 Flash$15.00
Which should you choose?
Which is better: Muse Spark 1.1, DeepSeek-R1 or Gemini 3.6 Flash?
It is close. Our weighted score puts them within 3 points (Gemini 3.6 Flash 73/100, Muse Spark 1.1 70/100, DeepSeek-R1 51/100), so choose by what matters most for your work: Gemini 3.6 Flash for raw capability and DeepSeek-R1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Muse Spark 1.1, DeepSeek-R1 or Gemini 3.6 Flash?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Gemini 3.6 Flash costs $0.75 input / $3.75 output per million tokens (official Google API price); Muse Spark 1.1 costs $1.25 input / $4.25 output per million tokens (official Meta API price). At a typical mix of three input tokens to one output token, that is $1.18 per million tokens for DeepSeek-R1 versus $1.50 for Gemini 3.6 Flash (1.3× as much) and $2.00 for Muse Spark 1.1 (1.7× as much).
Which scores higher on benchmarks?
Gemini 3.6 Flash scores higher on the Capabilities Index (ECI): Gemini 3.6 Flash 154.3 (#34 of 148), Muse Spark 1.1 154.3 (#35 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (152.6–156.3 vs 152.2–157.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Muse Spark 1.1, DeepSeek-R1 and Gemini 3.6 Flash yet, so there is no like-for-like coding score. On overall capability, Gemini 3.6 Flash leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Muse Spark 1.1 and Gemini 3.6 Flash have the largest context windows (1,048,576 and 1,048,576 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: Muse Spark 1.1 up to 131,072, DeepSeek-R1 up to 32,768, Gemini 3.6 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Muse Spark 1.1 accepts text, images, PDFs and video; DeepSeek-R1 accepts text; Gemini 3.6 Flash accepts text, images, PDFs, audio and video. Gemini 3.6 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 publishes its weights and can be self-hosted; Muse Spark 1.1 and Gemini 3.6 Flash is proprietary.
Which is newer?
Gemini 3.6 Flash is the newest, released Jul 21, 2026. Muse Spark 1.1 came out Jul 9, 2026; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Gemini 3.6 Flash Mar 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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