DeepSeek-R1 vs Muse Spark 1.1 vs Qwen3 235B-A22B
Muse Spark 1.1 comes out ahead, 70 to 51 and 51 on our weighted score, though DeepSeek-R1 is 41% cheaper per token.
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
MetaMuse Spark 1.1
70/100- ECI154.3
- Price$1.25 / $4.25
- Context1.05M
Alibaba (Qwen)Qwen3 235B-A22B
51/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
Muse Spark 1.1 is our pick
Muse Spark 1.1 is the better all-round choice, scoring 70/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMuse Spark 1.1Capabilities Index (ECI): Muse Spark 1.1 154.3 · Qwen3 235B-A22B 139.4 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Qwen3 235B-A22B $1.23 · Muse Spark 1.1 $2.00 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.1Muse Spark 1.1 1,048,576 · Qwen3 235B-A22B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsMuse Spark 1.1DeepSeek-R1: Text · Muse Spark 1.1: Text, Images, PDFs, Video · Qwen3 235B-A22B: Text
- Self-hostingDeepSeek-R1 and Qwen3 235B-A22BPublishes downloadable weights
| Measure | Weight | DeepSeek-R1 | Muse Spark 1.1 | Qwen3 235B-A22B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 84 | 65 |
| Price | 25% | 47 | 36 | 46 |
| Inputs & features | 15% | 35 | 90 | 35 |
| Context window | 10% | 24 | 61 | 24 |
| Overall | 100% | 51/100 | 70/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | Muse Spark 1.1Meta | Qwen3 235B-A22BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.0 | 154.3 (best) | 139.4 |
| ECI rank | #104 of 148 | #35 of 148 (best) | #103 of 148 |
| GPQA DiamondGraduate-level science questions | 71.7% (best) | — | 70.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | — | — |
| SimpleQA VerifiedShort factual questions | — | 57.8% | — |
| Price per million tokens | |||
| Input | $0.70 (best) | $1.25 | $0.70 (best) |
| Output | $2.60 (best) | $4.25 | $2.80 |
| Cached input | — | $0.15 | — |
| Blended (3:1) | $1.18 (best) | $2.00 | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Meta API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 32,768 tokens | 131,072 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yesminimal · low · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | muse-spark-1.1 | qwen3-235b-a22b |
| API providers | 12 | 13 (best) | 7 |
| Released | Jan 20, 2025 | Jul 9, 2026 | Apr 28, 2025 |
| Knowledge cutoff | Jul 2024 | — | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek-R1$12.20
- Muse Spark 1.1$21.00
- Qwen3 235B-A22B$12.60
Which should you choose?
Which is better: DeepSeek-R1, Muse Spark 1.1 or Qwen3 235B-A22B?
Muse Spark 1.1 is the better all-round choice, scoring 70/100 against Qwen3 235B-A22B (51) and DeepSeek-R1 (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1, Muse Spark 1.1 or Qwen3 235B-A22B?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Qwen3 235B-A22B costs $0.70 input / $2.80 output per million tokens (official Alibaba 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 $1.23 for Qwen3 235B-A22B (1× as much) and $2.00 for Muse Spark 1.1 (1.7× as much).
Which scores higher on benchmarks?
Muse Spark 1.1 scores higher on the Capabilities Index (ECI): Muse Spark 1.1 154.3 (#35 of 148), Qwen3 235B-A22B 139.4 (#103 of 148) and DeepSeek-R1 139.0 (#104 of 148). Their confidence ranges do not overlap (152.2–157.1 vs 135.2–140.8), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, Muse Spark 1.1 and Qwen3 235B-A22B yet, so there is no like-for-like coding score. On overall capability, Muse Spark 1.1 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 has the largest context window at 1,048,576 tokens, against 131,072 for Qwen3 235B-A22B and 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, Muse Spark 1.1 up to 131,072, Qwen3 235B-A22B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Muse Spark 1.1 accepts text, images, PDFs and video; Qwen3 235B-A22B accepts text. Muse Spark 1.1 handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 and Qwen3 235B-A22B publishes its weights and can be self-hosted; Muse Spark 1.1 is proprietary.
Which is newer?
Muse Spark 1.1 is the newest, released Jul 9, 2026. Qwen3 235B-A22B came out Apr 28, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Qwen3 235B-A22B Apr 2025.
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.
