GLM-5.3 vs Muse Spark 1.2 vs Nemotron 3.5 Super 120B A12B
Too close to call on our weighted score (Nemotron 3.5 Super 120B A12B 59, Muse Spark 1.2 59, GLM-5.3 42). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5.3
42/100- ECI156.2
- Price$1.40 / $4.40
- Context1M
MetaMuse Spark 1.2
59/100- ECI155.2
- Price$1.25 / $4.25
- Context1.05M
NVIDIANemotron 3.5 Super 120B A12B
59/100- ECI—
- Price$0.20 / $0.50
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (Nemotron 3.5 Super 120B A12B 59/100, Muse Spark 1.2 59/100, GLM-5.3 42/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
- Longest contextMuse Spark 1.2 and Nemotron 3.5 Super 120B A12BMuse Spark 1.2 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 · GLM-5.3 1,000,000 tokens
- Widest inputsMuse Spark 1.2GLM-5.3: Text · Muse Spark 1.2: Text, Images, PDFs, Audio, Video · Nemotron 3.5 Super 120B A12B: Text
- Self-hostingGLM-5.3 and Nemotron 3.5 Super 120B A12BPublishes downloadable weights
| Measure | Weight | GLM-5.3 | Muse Spark 1.2 | Nemotron 3.5 Super 120B A12B |
|---|---|---|---|---|
| Price | 50% | 33 | 34 | 73 |
| Inputs & features | 30% | 45 | 100 | 35 |
| Context window | 20% | 60 | 61 | 61 |
| Overall | 100% | 42/100 | 59/100 | 59/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Muse Spark 1.2Meta | Nemotron 3.5 Super 120B A12BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 156.2 (best) | 155.2 | — |
| ECI rank | #33 of 165 (best) | #38 of 165 | — |
| GPQA DiamondGraduate-level science questions | 90.9% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | — | — |
| SimpleQA VerifiedShort factual questions | 41.0% | 60.3% (best) | — |
| Price per million tokens | |||
| Input | $1.40 | $1.25 | $0.20 (best) |
| Output | $4.40 | $4.25 | $0.50 (best) |
| Cached input | $0.26 | $0.15 (best) | — |
| Blended (3:1) | $2.15 | $2.00 | $0.275 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Meta API | Median of 1 providers |
| Limits | |||
| Context window | 1,000,000 tokens | 1,048,576 tokens (best) | 1,048,576 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 131,072 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · high · max | Yesminimal · low · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5.3 | muse-spark-1.2 | — |
| API providers | 83 (best) | 18 | 1 |
| Released | Aug 14, 2026 | Aug 5, 2026 | Oct 15, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.3$22.80
- Muse Spark 1.2$21.00
- Nemotron 3.5 Super 120B A12B$3.00
Which should you choose?
Which is better: GLM-5.3, Muse Spark 1.2 or Nemotron 3.5 Super 120B A12B?
It is close. Our weighted score puts them within a point (Nemotron 3.5 Super 120B A12B 59/100, Muse Spark 1.2 59/100, GLM-5.3 42/100), so choose by what matters most for your work: Nemotron 3.5 Super 120B A12B on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-5.3, Muse Spark 1.2 or Nemotron 3.5 Super 120B A12B?
Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). Muse Spark 1.2 costs $1.25 input / $4.25 output per million tokens (official Meta API price); GLM-5.3 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $2.00 for Muse Spark 1.2 (7.3× as much) and $2.15 for GLM-5.3 (7.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5.3 has an ECI of 156.2, Muse Spark 1.2 has an ECI of 155.2 and Nemotron 3.5 Super 120B A12B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3, Muse Spark 1.2 and Nemotron 3.5 Super 120B A12B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Muse Spark 1.2 and Nemotron 3.5 Super 120B A12B have the largest context windows (1,048,576 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: GLM-5.3 up to 131,072, Muse Spark 1.2 up to 131,072, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Muse Spark 1.2 accepts text, images, PDFs, audio and video; Nemotron 3.5 Super 120B A12B accepts text. Muse Spark 1.2 handles the widest range of inputs.
Are any of these open source?
GLM-5.3 and Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; Muse Spark 1.2 is proprietary.
Which is newer?
Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 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 Nov 2, 2026.
