Head-to-Head Model Comparison

Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen 2.5 Coder 32BProfile

DeepSeek-Coder-V2 236BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen 2.5 Coder 32B

Spesialis Kode Open-Weights

Qwen 2.5 Coder 32B dari Alibaba adalah model AI yang dirancang untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), menghadirkan jendela konteks 128K, kecepatan 78 token/detik, indeks kecerdasan 43/100,

Intelligence Index43 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.6%

DeepSeek

DeepSeek-Coder-V2 236B

Model Dasar Bobot Terbuka

DeepSeek-Coder-V2 236B dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 74 token/detik, indeks kecerdasan 42/100, serta t

Intelligence Index42 / 100

Input Token:

$0.14 / 1M

Output Token:

$0.28 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42.1%

Perbedaan Utama Sekilas

DeepSeek-Coder-V2 236B is 1.9x cheaper for API usage (input + output tokens combined).
Qwen 2.5 Coder 32B is faster at 78 tok/s vs 74 tok/s.
Qwen 2.5 Coder 32B leads on SWE-bench coding (48.6% vs 42.1%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Qwen 2.5 Coder 32B	DeepSeek-Coder-V2 236B
Intelligence Index Composite reasoning & knowledge score (0-100)	43	42
Provider	Alibaba	DeepSeek
Input Price Cost per million input tokens	$0.20 / 1M	$0.14 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.28 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.13 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	78 tok/s	74 tok/s
SWE-bench Verified Real-world software engineering	48.6%	42.1%
GPQA Diamond Graduate-level reasoning	48.2%	49.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.04 / 1M	$0.01 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Qwen 2.5 Coder 32B

+6.5% SWE-bench lead

Cost

DeepSeek-Coder-V2 236B

1.9x cheaper monthly runtime

Speed

Qwen 2.5 Coder 32B

+5% faster generation

Context

Tied

128K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Qwen 2.5 Coder 32BDeepSeek-Coder-V2 236B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

48.6%vs42.1%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

48.2%vs49.5%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

68.9%vs69.8%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1268ptsvs1272pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

6.8%vs5.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

32.4%vs27.8%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

31.8%vs29.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

53.2%vs48.6%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

55.4%vs61.2%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

65.4%vs66.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs74 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen 2.5 Coder 32B0.54s

DeepSeek-Coder-V2 236B0.51s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen 2.5 Coder 32B6.95s

DeepSeek-Coder-V2 236B7.27s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen 2.5 Coder 32B Monthly API Bill

$0.90 / month

$0.2/1M in · $0.6/1M out

DeepSeek-Coder-V2 236B Monthly API Bill

$0.48 / month

$0.14/1M in · $0.28/1M out

DeepSeek-Coder-V2 236B saves approximately $0.42 / month (46%) under this workload.1.9x Savings

Qwen 2.5 Coder 32B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 48.6% · GPQA Diamond 48.2%)
Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 540 ms
Bobot terbuka (32B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

DeepSeek-Coder-V2 236B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 42.1% · GPQA Diamond 49.5%)
Kecepatan generasi tinggi 74 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
Bobot terbuka (236B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Qwen 2.5 Coder 32B	DeepSeek-Coder-V2 236B
Cost per Task Estimated USD to finish one task	$0.18	$0.14
Output Tokens / Task Tokens generated per task	35K	30K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen 2.5 Coder 32B	DeepSeek-Coder-V2 236B
Official API Model ID (SDK)	qwen-2-5-coder-32b-instruct	deepseek-chat
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-11	2024-06
Knowledge Cutoff	September 2024	December 2023
Architecture	Dense Code-Specialized Transformer	236B Mixture-of-Experts (21B active)
Parameters	32B	236B
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($0.2/1M in) across full 128K window	Flat rate ($0.14/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.04/1M tokens	Automatic prefix caching at $0.014/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 146 GB VRAM (4-bit Quantized) · 507 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text
Output Modalities	text	text

Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B: Jawaban Cepat (FAQ)

Which is more intelligent, Qwen 2.5 Coder 32B or DeepSeek-Coder-V2 236B?

Qwen 2.5 Coder 32B is more intelligent. It scores 43 compared with 42 on the composite Intelligence Index.

Which model is faster?

Qwen 2.5 Coder 32B is faster, generating 78 tokens per second compared with 74 tokens per second.

Which model is cheaper to run?

DeepSeek-Coder-V2 236B is cheaper at $0.07 per 1M tokens blended, compared with $0.13 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek-Coder-V2 236B has lower latency, with a time to first token of 0.51s compared with 0.54s.

Kesimpulan

Baik Qwen 2.5 Coder 32B (Sangat cocok untuk pengembangan perangkat lunak dan pembuatan kode dengan bobot terbuka (open-weights), sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek-Coder-V2 236B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen 2.5 Coder 32B unggul dalam penalaran dan pemrograman, Qwen 2.5 Coder 32B memberikan kecepatan generasi token lebih tinggi, dan DeepSeek-Coder-V2 236B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen 2.5 Coder 32B atau DeepSeek-Coder-V2 236B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen 2.5 Coder 32B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B?+

Qwen 2.5 Coder 32B mengenakan biaya $0.20 / $0.60 per 1M token input/output, sedangkan DeepSeek-Coder-V2 236B sebesar $0.14 / $0.28. Secara keseluruhan, DeepSeek-Coder-V2 236B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Qwen 2.5 Coder 32B mendukung konteks 128K token dibandingkan 128K token pada DeepSeek-Coder-V2 236B, sementara Qwen 2.5 Coder 32B unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B

Ganti & Bandingkan Model Apa Saja

Qwen 2.5 Coder 32B

DeepSeek-Coder-V2 236B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen 2.5 Coder 32B · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek-Coder-V2 236B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen 2.5 Coder 32B vs DeepSeek-Coder-V2 236B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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