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Comparison · 3 models · Updated Oct 4, 2026

GPT-5 Mini vs Kimi K2 Thinking vs Qwen3 14B

GPT-5 Mini comes out ahead, 65 to 58 and 54 on our weighted score, though Qwen3 14B is 11% cheaper per token.

  1. Our pick

    OpenAI

    GPT-5 Mini

    Released Aug 7, 2025

    65/100
    • ECI145.5
    • Price$0.25 / $2.00
    • Context400K
  2. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
  3. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

    54/100
    • ECI138.2
    • Price$0.35 / $1.40
    • Context131K
01 — Verdict

GPT-5 Mini is our pick

GPT-5 Mini is the better all-round choice, scoring 65/100 against Kimi K2 Thinking (58) and Qwen3 14B (54). It leads on inputs & features and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2 ThinkingCapabilities Index (ECI): Kimi K2 Thinking 146.0 · GPT-5 Mini 145.5 · Qwen3 14B 138.2
  • Lowest priceQwen3 14BQwen3 14B $0.613 · GPT-5 Mini $0.688 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextGPT-5 MiniGPT-5 Mini 400,000 · Kimi K2 Thinking 262,144 · Qwen3 14B 131,072 tokens
  • Widest inputsGPT-5 MiniGPT-5 Mini: Text, Images · Kimi K2 Thinking: Text · Qwen3 14B: Text
  • Self-hostingKimi K2 Thinking and Qwen3 14BPublishes downloadable weights
How the score is built
MeasureWeightGPT-5 MiniKimi K2 ThinkingQwen3 14B
CapabilityCapabilities Index (ECI)50%727363
Price25%584860
Inputs & features15%703535
Context window10%443724
Overall100%65/10058/10054/100
02 — Side by side

Every spec in one table

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

GPT-5 Mini vs Kimi K2 Thinking vs Qwen3 14B specifications side by side
SpecificationGPT-5 MiniOpenAIKimi K2 ThinkingMoonshot AIQwen3 14BAlibaba (Qwen)
Capability
Capabilities Index (ECI)145.5146.0 (best)138.2
ECI rank#77 of 148#72 of 148 (best)#107 of 148
GPQA DiamondGraduate-level science questions75.0%84.2% (best)63.8%
FrontierMath Tiers 1–3Research-level mathematics46.7%——
OTIS Mock AIME 2024–2025Competition mathematics86.7% (best)83.1%66.4%
SWE-bench VerifiedFixing real GitHub issues64.7%——
SimpleQA VerifiedShort factual questions21.6%——
Price per million tokens
Input$0.25 (best)$0.60$0.35
Output$2.00$2.50$1.40 (best)
Cached input$0.025——
Blended (3:1)$0.688$1.07$0.613 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIMedian of 10 providersOfficial Alibaba API
Limits
Context window400,000 tokens (best)262,144 tokens131,072 tokens
Max output128,000 tokens262,144 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesminimal · low · medium · highYesYes
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-5-mini—qwen3-14b
API providers23 (best)101
ReleasedAug 7, 2025Nov 6, 2025Apr 29, 2025
Knowledge cutoffMay 30, 2024Aug 2024Apr 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.

  • GPT-5 Mini$6.50
  • Kimi K2 Thinking$11.00
  • Qwen3 14B$6.30
04 — Questions

Which should you choose?

Which is better: GPT-5 Mini, Kimi K2 Thinking or Qwen3 14B?

GPT-5 Mini is the better all-round choice, scoring 65/100 against Kimi K2 Thinking (58) and Qwen3 14B (54). It leads on inputs & features and context window. Qwen3 14B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-5 Mini, Kimi K2 Thinking or Qwen3 14B?

Qwen3 14B is cheaper at $0.35 input / $1.40 output per million tokens (official Alibaba API price). GPT-5 Mini costs $0.25 input / $2.00 output per million tokens (official OpenAI API price); Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $0.613 per million tokens for Qwen3 14B versus $0.688 for GPT-5 Mini (1.1× as much) and $1.07 for Kimi K2 Thinking (1.8× as much).

Which scores higher on benchmarks?

Kimi K2 Thinking scores higher on the Capabilities Index (ECI): Kimi K2 Thinking 146.0 (#72 of 148), GPT-5 Mini 145.5 (#77 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (143.4–147.6 vs 143.6–147.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K2 Thinking 84.2%, GPT-5 Mini 75.0%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — GPT-5 Mini 86.7%, Kimi K2 Thinking 83.1%, Qwen3 14B 66.4%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Thinking and Qwen3 14B yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Thinking 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 Mini has the largest context window at 400,000 tokens, against 262,144 for Kimi K2 Thinking and 131,072 for Qwen3 14B. Maximum output per response: GPT-5 Mini up to 128,000, Kimi K2 Thinking up to 262,144, Qwen3 14B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

GPT-5 Mini accepts text and images; Kimi K2 Thinking accepts text; Qwen3 14B accepts text. GPT-5 Mini handles the widest range of inputs.

Are any of these open source?

Kimi K2 Thinking and Qwen3 14B publishes its weights and can be self-hosted; GPT-5 Mini is proprietary.

Which is newer?

Kimi K2 Thinking is the newest, released Nov 6, 2025. GPT-5 Mini came out Aug 7, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: GPT-5 Mini May 30, 2024, Kimi K2 Thinking Aug 2024, 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.