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Comparison · 3 models · Updated Nov 2, 2026

AutoGLM Phone 9B Multilingual vs Gemma 4 E2B IT vs Qwen3.5 4B

Too close to call on our weighted score (Qwen3.5 4B 78, Gemma 4 E2B IT 76, AutoGLM Phone 9B Multilingual 60). The right pick depends on what you value most.

  1. Z.ai (Zhipu)

    AutoGLM Phone 9B Multilingual

    Released Dec 10, 2025

    60/100
    • ECI—
    • Price$0.035 / $0.138
    • Context66K
    Full specs Remove AutoGLM Phone 9B Multilingual

  2. Google

    Gemma 4 E2B IT

    Released Apr 2, 2026

    76/100
    • ECI—
    • Price$0.04 / $0.08
    • Context131K
    Full specs Remove Gemma 4 E2B IT

  3. Alibaba (Qwen)

    Qwen3.5 4B

    Released Mar 2, 2026

    78/100
    • ECI—
    • Price$0.04 / $0.07
    • Context262K
    Full specs Remove Qwen3.5 4B
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Qwen3.5 4B 78/100, Gemma 4 E2B IT 76/100, AutoGLM Phone 9B Multilingual 60/100), so choose by what matters most for your work: Qwen3.5 4B on price and Qwen3.5 4B for long inputs. 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 priceQwen3.5 4BQwen3.5 4B $0.048 · Gemma 4 E2B IT $0.05 · AutoGLM Phone 9B Multilingual $0.061 per 1M tokens (3:1 blend)
  • Longest contextQwen3.5 4BQwen3.5 4B 262,144 · Gemma 4 E2B IT 131,072 · AutoGLM Phone 9B Multilingual 65,536 tokens
  • Widest inputsGemma 4 E2B IT and Qwen3.5 4BAutoGLM Phone 9B Multilingual: Text, Images · Gemma 4 E2B IT: Text, Images, Audio · Qwen3.5 4B: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightAutoGLM Phone 9B MultilingualGemma 4 E2B ITQwen3.5 4B
Price50%100100100
Inputs & features30%257070
Context window20%122537
Overall100%60/10076/10078/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

AutoGLM Phone 9B Multilingual vs Gemma 4 E2B IT vs Qwen3.5 4B specifications side by side
SpecificationAutoGLM Phone 9B MultilingualZ.ai (Zhipu)Gemma 4 E2B ITGoogleQwen3.5 4BAlibaba (Qwen)
Capability
Capabilities Index (ECI)———
ECI rank———
OTIS Mock AIME 2024–2025Competition mathematics——55.8%
Price per million tokens
Input$0.035 (best)$0.04$0.04
Output$0.138$0.08$0.07 (best)
Cached input———
Blended (3:1)$0.061$0.05$0.048 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 2 providersMedian of 1 providersMedian of 3 providers
Limits
Context window65,536 tokens131,072 tokens262,144 tokens (best)
Max output65,536 tokens8,192 tokens81,920 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesYes
PDFsNoNoNo
AudioNoYesNo
VideoNoNoYes
ReasoningNoYesYes
Tool callingNoYesYes
Structured outputNoNoNo
Availability
WeightsOpenMITOpenOpenApache-2.0
API model ID———
API providers215 (best)
ReleasedDec 10, 2025Apr 2, 2026Mar 2, 2026
Knowledge cutoff———
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.

  • AutoGLM Phone 9B Multilingual$0.626
  • Gemma 4 E2B IT$0.56
  • Qwen3.5 4B$0.54
04 — Questions

Which should you choose?

Which is better: AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT or Qwen3.5 4B?

It is close. Our weighted score puts them within 2 points (Qwen3.5 4B 78/100, Gemma 4 E2B IT 76/100, AutoGLM Phone 9B Multilingual 60/100), so choose by what matters most for your work: Qwen3.5 4B on price and Qwen3.5 4B for long inputs. 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, AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT or Qwen3.5 4B?

Qwen3.5 4B is cheaper at $0.04 input / $0.07 output per million tokens (median across 3 API providers). Gemma 4 E2B IT costs $0.04 input / $0.08 output per million tokens (median across 1 API provider); AutoGLM Phone 9B Multilingual costs $0.035 input / $0.138 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.048 per million tokens for Qwen3.5 4B versus $0.05 for Gemma 4 E2B IT (1.1× as much) and $0.061 for AutoGLM Phone 9B Multilingual (1.3× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. AutoGLM Phone 9B Multilingual has not been scored yet, Gemma 4 E2B IT has not been scored yet and Qwen3.5 4B has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for AutoGLM Phone 9B Multilingual, Gemma 4 E2B IT and Qwen3.5 4B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that AutoGLM Phone 9B Multilingual does not support tool calling, which most coding agents need.

Which has the bigger context window?

Qwen3.5 4B has the largest context window at 262,144 tokens, against 131,072 for Gemma 4 E2B IT and 65,536 for AutoGLM Phone 9B Multilingual. Maximum output per response: AutoGLM Phone 9B Multilingual up to 65,536, Gemma 4 E2B IT up to 8,192, Qwen3.5 4B up to 81,920 tokens.

Which can read images, PDFs, audio or video?

AutoGLM Phone 9B Multilingual accepts text and images; Gemma 4 E2B IT accepts text, images and audio; Qwen3.5 4B accepts text, images and video. Gemma 4 E2B IT handles the widest range of inputs.

Are any of these open source?

Yes, all three publish their weights (MIT and Apache-2.0), so you can self-host them.

Which is newer?

Gemma 4 E2B IT is the newest, released Apr 2, 2026. Qwen3.5 4B came out Mar 2, 2026; AutoGLM Phone 9B Multilingual came out Dec 10, 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 Nov 2, 2026.

05 — Keep comparing

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