ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Muse Spark 1.2 vs GPT-5.6 Sol vs GLM-5.3

Muse Spark 1.2 comes out ahead, 71 to 66 and 63 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Meta

    Muse Spark 1.2

    Released Aug 5, 2026

    71/100
    • ECI155.2
    • Price$1.25 / $4.25
    • Context1.05M
  2. OpenAI

    GPT-5.6 Sol

    Released Jul 9, 2026

    66/100
    • ECI162.6
    • Price$4.00 / $20.00
    • Context1.05M
  3. Z.ai (Zhipu)

    GLM-5.3

    Released Aug 14, 2026

    63/100
    • ECI156.2
    • Price$1.40 / $4.40
    • Context1M
01 — Verdict

Muse Spark 1.2 is our pick

Muse Spark 1.2 is the better all-round choice, scoring 71/100 against GPT-5.6 Sol (66) and GLM-5.3 (63). It leads on inputs & features. GPT-5.6 Sol wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGPT-5.6 SolCapabilities Index (ECI): GPT-5.6 Sol 162.6 · GLM-5.3 156.2 · Muse Spark 1.2 155.2
  • Lowest priceMuse Spark 1.2Muse Spark 1.2 $2.00 · GLM-5.3 $2.15 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
  • Longest contextGPT-5.6 Sol and Muse Spark 1.2GPT-5.6 Sol 1,050,000 · Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 tokens
  • Widest inputsMuse Spark 1.2Muse Spark 1.2: Text, Images, PDFs, Audio, Video · GPT-5.6 Sol: Text, Images, PDFs · GLM-5.3: Text
  • Self-hostingGLM-5.3Publishes downloadable weights
How the score is built
MeasureWeightMuse Spark 1.2GPT-5.6 SolGLM-5.3
CapabilityCapabilities Index (ECI)50%839284
Price25%34733
Inputs & features15%1008045
Context window10%616160
Overall100%71/10066/10063/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Muse Spark 1.2 vs GPT-5.6 Sol vs GLM-5.3 specifications side by side
SpecificationMuse Spark 1.2MetaGPT-5.6 SolOpenAIGLM-5.3Z.ai (Zhipu)
Capability
Capabilities Index (ECI)155.2162.6 (best)156.2
ECI rank#38 of 165#12 of 165 (best)#33 of 165
GPQA DiamondGraduate-level science questions—93.5% (best)90.9%
FrontierMath Tiers 1–3Research-level mathematics—89.1% (best)68.8%
OTIS Mock AIME 2024–2025Competition mathematics—100% (best)91.1%
SimpleQA VerifiedShort factual questions60.3%69.7% (best)41.0%
Price per million tokens
Input$1.25 (best)$4.00$1.40
Output$4.25 (best)$20.00$4.40
Cached input$0.15 (best)$0.40$0.26
Blended (3:1)$2.00 (best)$8.00$2.15
Long-context rateSame rateOver 272K: $8.00 / $30.00Same rate
Price sourceOfficial Meta APIOfficial OpenAI APIOfficial Z.AI API
Limits
Context window1,048,576 tokens1,050,000 tokens (best)1,000,000 tokens
Max output131,072 tokens (best)128,000 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsYesYesNo
AudioYesNoNo
VideoYesNoNo
ReasoningYesminimal · low · medium · high · xhighYeslow · medium · high · xhigh · maxYeslow · high · max
Tool callingYesYesYes
Structured outputYesYesYes
Availability
WeightsProprietaryProprietaryOpen
API model IDmuse-spark-1.2gpt-5.6-solglm-5.3
API providers184983 (best)
ReleasedAug 5, 2026Jul 9, 2026Aug 14, 2026
Knowledge cutoff—Feb 16, 2026—
03 — Cost

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 1.2$21.00
  • GPT-5.6 Sol$80.00
  • GLM-5.3$22.80
04 — Questions

Which should you choose?

Which is better: Muse Spark 1.2, GPT-5.6 Sol or GLM-5.3?

Muse Spark 1.2 is the better all-round choice, scoring 71/100 against GPT-5.6 Sol (66) and GLM-5.3 (63). It leads on inputs & features. GPT-5.6 Sol wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Muse Spark 1.2, GPT-5.6 Sol or GLM-5.3?

Muse Spark 1.2 is cheaper at $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); GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $2.00 per million tokens for Muse Spark 1.2 versus $2.15 for GLM-5.3 (1.1× as much) and $8.00 for GPT-5.6 Sol (4× as much).

Which scores higher on benchmarks?

GPT-5.6 Sol scores higher on the Capabilities Index (ECI): GPT-5.6 Sol 162.6 (#12 of 165), GLM-5.3 156.2 (#33 of 165) and Muse Spark 1.2 155.2 (#38 of 165). Their confidence ranges do not overlap (159.6–166.2 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: SimpleQA Verified — GPT-5.6 Sol 69.7%, Muse Spark 1.2 60.3%, GLM-5.3 41.0%.

Which is better for coding?

There are no published SWE-bench Verified results for Muse Spark 1.2, GPT-5.6 Sol and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 Sol 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?

GPT-5.6 Sol and Muse Spark 1.2 have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for GLM-5.3. Maximum output per response: Muse Spark 1.2 up to 131,072, GPT-5.6 Sol up to 128,000, GLM-5.3 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Muse Spark 1.2 accepts text, images, PDFs, audio and video; GPT-5.6 Sol accepts text, images and PDFs; GLM-5.3 accepts text. Muse Spark 1.2 handles the widest range of inputs.

Are any of these open source?

GLM-5.3 publishes its weights and can be self-hosted; Muse Spark 1.2 and GPT-5.6 Sol is proprietary.

Which is newer?

GLM-5.3 is the newest, released Aug 14, 2026. Muse Spark 1.2 came out Aug 5, 2026; GPT-5.6 Sol came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Sol Feb 16, 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.