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Comparison · 3 models · Updated Nov 2, 2026

Gemma 4 31B IT vs GLM-4.7 vs Qwen3.5 Flash

Qwen3.5 Flash comes out ahead, 73 to 68 and 55 on our weighted score, and it is the cheaper option too.

  1. Google

    Gemma 4 31B IT

    Released Apr 2, 2026

    68/100
    • ECI143.1
    • Price$0.14 / $0.40
    • Context262K
  2. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    55/100
    • ECI143.7
    • Price$0.60 / $2.20
    • Context205K
  3. Our pick

    Alibaba (Qwen)

    Qwen3.5 Flash

    Released Feb 23, 2026

    73/100
    • ECI143.6
    • Price$0.10 / $0.40
    • Context1M
01 — Verdict

Qwen3.5 Flash is our pick

Qwen3.5 Flash is the better all-round choice, scoring 73/100 against Gemma 4 31B IT (68) and GLM-4.7 (55). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.7 · Qwen3.5 Flash 143.6 · Gemma 4 31B IT 143.1
  • Lowest priceQwen3.5 FlashQwen3.5 Flash $0.175 · Gemma 4 31B IT $0.205 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · Gemma 4 31B IT 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsQwen3.5 FlashGemma 4 31B IT: Text, Images · GLM-4.7: Text · Qwen3.5 Flash: Text, Images, Video
  • Self-hostingGemma 4 31B IT and GLM-4.7Publishes downloadable weights
How the score is built
MeasureWeightGemma 4 31B ITGLM-4.7Qwen3.5 Flash
CapabilityCapabilities Index (ECI)50%686969
Price25%794882
Inputs & features15%703580
Context window10%373260
Overall100%68/10055/10073/100
02 — Side by side

Every spec in one table

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

Gemma 4 31B IT vs GLM-4.7 vs Qwen3.5 Flash specifications side by side
SpecificationGemma 4 31B ITGoogleGLM-4.7Z.ai (Zhipu)Qwen3.5 FlashAlibaba (Qwen)
Capability
Capabilities Index (ECI)143.1143.7 (best)143.6
ECI rank#96 of 165#94 of 165 (best)#95 of 165
GPQA DiamondGraduate-level science questions75.8%83.3% (best)82.3%
FrontierMath Tiers 1–3Research-level mathematics——18.3%
OTIS Mock AIME 2024–2025Competition mathematics73.3%83.3%84.4% (best)
SimpleQA VerifiedShort factual questions10.4%32.2% (best)20.3%
Price per million tokens
Input$0.14$0.60$0.10 (best)
Output$0.40 (best)$2.20$0.40 (best)
Cached input—$0.11$0.01 (best)
Blended (3:1)$0.205$1.00$0.175 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 36 providersOfficial Z.AI APIOfficial Alibaba API
Limits
Context window262,144 tokens204,800 tokens1,000,000 tokens (best)
Max output32,768 tokens131,072 tokens (best)65,536 tokens
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsOpenOpenProprietary
API model IDgemma-4-31b-itglm-4.7qwen3.5-flash
API providers46 (best)319
ReleasedApr 2, 2026Dec 22, 2025Feb 23, 2026
Knowledge cutoffJan 2025Apr 2025—
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.

  • Gemma 4 31B IT$2.20
  • GLM-4.7$10.40
  • Qwen3.5 Flash$1.80
04 — Questions

Which should you choose?

Which is better: Gemma 4 31B IT, GLM-4.7 or Qwen3.5 Flash?

Qwen3.5 Flash is the better all-round choice, scoring 73/100 against Gemma 4 31B IT (68) and GLM-4.7 (55). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Gemma 4 31B IT, GLM-4.7 or Qwen3.5 Flash?

Qwen3.5 Flash is cheaper at $0.10 input / $0.40 output per million tokens (official Alibaba API price). Gemma 4 31B IT costs $0.14 input / $0.40 output per million tokens (median across 36 API providers); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.175 per million tokens for Qwen3.5 Flash versus $0.205 for Gemma 4 31B IT (1.2× as much) and $1.00 for GLM-4.7 (5.7× as much).

Which scores higher on benchmarks?

GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.7 (#94 of 165), Qwen3.5 Flash 143.6 (#95 of 165) and Gemma 4 31B IT 143.1 (#96 of 165). The confidence ranges of the top two overlap (141.4–145.8 vs 141.4–145.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, Qwen3.5 Flash 82.3%, Gemma 4 31B IT 75.8%; OTIS Mock AIME 2024–2025 — Qwen3.5 Flash 84.4%, GLM-4.7 83.3%, Gemma 4 31B IT 73.3%; SimpleQA Verified — GLM-4.7 32.2%, Qwen3.5 Flash 20.3%, Gemma 4 31B IT 10.4%.

Which is better for coding?

There are no published SWE-bench Verified results for Gemma 4 31B IT, GLM-4.7 and Qwen3.5 Flash yet, so there is no like-for-like coding score. On overall capability, GLM-4.7 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?

Qwen3.5 Flash has the largest context window at 1,000,000 tokens, against 262,144 for Gemma 4 31B IT and 204,800 for GLM-4.7. Maximum output per response: Gemma 4 31B IT up to 32,768, GLM-4.7 up to 131,072, Qwen3.5 Flash up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Gemma 4 31B IT accepts text and images; GLM-4.7 accepts text; Qwen3.5 Flash accepts text, images and video. Qwen3.5 Flash handles the widest range of inputs.

Are any of these open source?

Gemma 4 31B IT and GLM-4.7 publishes its weights and can be self-hosted; Qwen3.5 Flash is proprietary.

Which is newer?

Gemma 4 31B IT is the newest, released Apr 2, 2026. Qwen3.5 Flash came out Feb 23, 2026; GLM-4.7 came out Dec 22, 2025. Knowledge cutoff: Gemma 4 31B IT Jan 2025, GLM-4.7 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 Nov 2, 2026.