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Comparison · 3 models · Updated Oct 4, 2026

DeepSeek-V3.1 vs GLM-4.7 vs Qwen3 14B

Too close to call on our weighted score (GLM-4.7 56, DeepSeek-V3.1 55, Qwen3 14B 54). The right pick depends on what you value most.

  1. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

    55/100
    • ECI139.9
    • Price$0.385 / $1.25
    • Context131K
    Full specs Remove DeepSeek-V3.1

  2. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    56/100
    • ECI143.5
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7

  3. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

    54/100
    • ECI138.2
    • Price$0.35 / $1.40
    • Context131K
    Full specs Remove Qwen3 14B
01 — Verdict

Too close to call

It is close. Our weighted score puts them within a point (GLM-4.7 56/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: GLM-4.7 for raw capability and DeepSeek-V3.1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-4.7Capabilities Index (ECI): GLM-4.7 143.5 · DeepSeek-V3.1 139.9 · Qwen3 14B 138.2
  • Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
  • Longest contextGLM-4.7GLM-4.7 204,800 · DeepSeek-V3.1 131,072 · Qwen3 14B 131,072 tokens
  • Widest inputsSame inputsDeepSeek-V3.1: Text · GLM-4.7: Text · Qwen3 14B: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightDeepSeek-V3.1GLM-4.7Qwen3 14B
CapabilityCapabilities Index (ECI)50%657063
Price25%605060
Inputs & features15%353535
Context window10%243224
Overall100%55/10056/10054/100
02 — Side by side

Every spec in one table

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

DeepSeek-V3.1 vs GLM-4.7 vs Qwen3 14B specifications side by side
SpecificationDeepSeek-V3.1DeepSeekGLM-4.7Z.ai (Zhipu)Qwen3 14BAlibaba (Qwen)
Capability
Capabilities Index (ECI)139.9143.5 (best)138.2
ECI rank#100 of 148#84 of 148 (best)#107 of 148
GPQA DiamondGraduate-level science questions—83.3% (best)63.8%
OTIS Mock AIME 2024–2025Competition mathematics—83.3% (best)66.4%
SimpleQA VerifiedShort factual questions—32.2%—
Price per million tokens
Input$0.385$0.60$0.35 (best)
Output$1.25 (best)$2.20$1.40
Cached input—$0.11—
Blended (3:1)$0.601 (best)$1.00$0.613
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 8 providersOfficial Z.AI APIOfficial Alibaba API
Limits
Context window131,072 tokens204,800 tokens (best)131,072 tokens
Max output8,192 tokens131,072 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenMIT LicenseOpenOpen
API model ID—glm-4.7qwen3-14b
API providers820 (best)1
ReleasedAug 21, 2025Dec 22, 2025Apr 29, 2025
Knowledge cutoff—Apr 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.

  • DeepSeek-V3.1$6.35
  • GLM-4.7$10.40
  • Qwen3 14B$6.30
04 — Questions

Which should you choose?

Which is better: DeepSeek-V3.1, GLM-4.7 or Qwen3 14B?

It is close. Our weighted score puts them within a point (GLM-4.7 56/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: GLM-4.7 for raw capability and DeepSeek-V3.1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, DeepSeek-V3.1, GLM-4.7 or Qwen3 14B?

DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price); 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 $0.601 per million tokens for DeepSeek-V3.1 versus $0.613 for Qwen3 14B (1× as much) and $1.00 for GLM-4.7 (1.7× as much).

Which scores higher on benchmarks?

GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.5 (#84 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (141.3–145.6 vs 136.1–143.3), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek-V3.1, GLM-4.7 and Qwen3 14B 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?

GLM-4.7 has the largest context window at 204,800 tokens, against 131,072 for DeepSeek-V3.1 and 131,072 for Qwen3 14B. Maximum output per response: DeepSeek-V3.1 up to 8,192, GLM-4.7 up to 131,072, Qwen3 14B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

DeepSeek-V3.1 accepts text; GLM-4.7 accepts text; Qwen3 14B accepts text. They handle the same number of input types.

Are any of these open source?

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

Which is newer?

GLM-4.7 is the newest, released Dec 22, 2025. DeepSeek-V3.1 came out Aug 21, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Qwen3 14B 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 Oct 4, 2026.

05 — Keep comparing

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