ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-5.3 vs GPT-6.1 Sol vs Muse Spark 1.2

Too close to call on our weighted score (GPT-6.1 Sol 72, Muse Spark 1.2 71, GLM-5.3 63). The right pick depends on what you value most.

  1. Z.ai (Zhipu)

    GLM-5.3

    Released Aug 14, 2026

    63/100
    • ECI156.2
    • Price$1.40 / $4.40
    • Context1M
  2. OpenAI

    GPT-6.1 Sol

    Released Sep 29, 2026New

    72/100
    • ECI167.8
    • Price$2.00 / $10.00
    • Context1.05M
  3. Meta

    Muse Spark 1.2

    Released Aug 5, 2026

    71/100
    • ECI155.2
    • Price$1.25 / $4.25
    • Context1.05M
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (GPT-6.1 Sol 72/100, Muse Spark 1.2 71/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-6.1 Sol for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGPT-6.1 SolCapabilities Index (ECI): GPT-6.1 Sol 167.8 · 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-6.1 Sol $4.00 per 1M tokens (3:1 blend)
  • Longest contextGPT-6.1 Sol and Muse Spark 1.2GPT-6.1 Sol 1,050,000 · Muse Spark 1.2 1,048,576 · GLM-5.3 1,000,000 tokens
  • Widest inputsMuse Spark 1.2GLM-5.3: Text · GPT-6.1 Sol: Text, Images, PDFs · Muse Spark 1.2: Text, Images, PDFs, Audio, Video
  • Self-hostingGLM-5.3Publishes downloadable weights
How the score is built
MeasureWeightGLM-5.3GPT-6.1 SolMuse Spark 1.2
CapabilityCapabilities Index (ECI)50%849983
Price25%332034
Inputs & features15%4580100
Context window10%606161
Overall100%63/10072/10071/100
02 — Side by side

Every spec in one table

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

GLM-5.3 vs GPT-6.1 Sol vs Muse Spark 1.2 specifications side by side
SpecificationGLM-5.3Z.ai (Zhipu)GPT-6.1 SolOpenAIMuse Spark 1.2Meta
Capability
Capabilities Index (ECI)156.2167.8 (best)155.2
ECI rank#33 of 165#4 of 165 (best)#38 of 165
GPQA DiamondGraduate-level science questions90.9%95.4% (best)—
FrontierMath Tiers 1–3Research-level mathematics68.8%93.7% (best)—
OTIS Mock AIME 2024–2025Competition mathematics91.1%100% (best)—
SimpleQA VerifiedShort factual questions41.0%73.9% (best)60.3%
Price per million tokens
Input$1.40$2.00$1.25 (best)
Output$4.40$10.00$4.25 (best)
Cached input$0.26$0.10 (best)$0.15
Blended (3:1)$2.15$4.00$2.00 (best)
Long-context rateSame rateOver 272K: $4.00 / $15.00Same rate
Price sourceOfficial Z.AI APIOfficial OpenAI APIOfficial Meta API
Limits
Context window1,000,000 tokens1,050,000 tokens (best)1,048,576 tokens
Max output131,072 tokens (best)128,000 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoYesYes
AudioNoNoYes
VideoNoNoYes
ReasoningYeslow · high · maxYeslow · medium · high · xhigh · maxYesminimal · low · medium · high · xhigh
Tool callingYesYesYes
Structured outputYesYesYes
Availability
WeightsOpenProprietaryProprietary
API model IDglm-5.3gpt-6.1-solmuse-spark-1.2
API providers83 (best)3318
ReleasedAug 14, 2026Sep 29, 2026Aug 5, 2026
Knowledge cutoff—Apr 30, 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.

  • GLM-5.3$22.80
  • GPT-6.1 Sol$40.00
  • Muse Spark 1.2$21.00
04 — Questions

Which should you choose?

Which is better: GLM-5.3, GPT-6.1 Sol or Muse Spark 1.2?

It is close. Our weighted score puts them within 1 points (GPT-6.1 Sol 72/100, Muse Spark 1.2 71/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-6.1 Sol for raw capability and Muse Spark 1.2 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-5.3, GPT-6.1 Sol or Muse Spark 1.2?

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-6.1 Sol costs $2.00 input / $10.00 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 $4.00 for GPT-6.1 Sol (2× as much).

Which scores higher on benchmarks?

GPT-6.1 Sol scores higher on the Capabilities Index (ECI): GPT-6.1 Sol 167.8 (#4 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 (164.3–172.5 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: SimpleQA Verified — GPT-6.1 Sol 73.9%, 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 GLM-5.3, GPT-6.1 Sol and Muse Spark 1.2 yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 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-6.1 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: GLM-5.3 up to 131,072, GPT-6.1 Sol up to 128,000, Muse Spark 1.2 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

GLM-5.3 accepts text; GPT-6.1 Sol accepts text, images and PDFs; Muse Spark 1.2 accepts text, images, PDFs, audio and video. 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; GPT-6.1 Sol and Muse Spark 1.2 is proprietary.

Which is newer?

GPT-6.1 Sol is the newest, released Sep 29, 2026. GLM-5.3 came out Aug 14, 2026; Muse Spark 1.2 came out Aug 5, 2026. Knowledge cutoff: GPT-6.1 Sol Apr 30, 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.