Head-to-Head Model Comparison

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium

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.

Qwen3-Coder 30B-A3B InstructProfile

Devstral MediumProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3-Coder 30B-A3B Instruct

Model Dasar Bobot Terbuka

Qwen3-Coder 30B-A3B Instruct dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 140 token/detik, indeks kecerdasan 76/100, s

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Mistral AI

Devstral Medium

Model Agen Otonom & Alat

Devstral Medium dari Mistral AI adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 128K, kecepatan 75 token/detik, indeks kecerdasan 52/1

Intelligence Index52 / 100

Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

51.5%

Perbedaan Utama Sekilas

Qwen3-Coder 30B-A3B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
Qwen3-Coder 30B-A3B Instruct has a larger context window (262K vs 128K tokens).
Qwen3-Coder 30B-A3B Instruct is faster at 140 tok/s vs 75 tok/s.

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

Feature	Qwen3-Coder 30B-A3B Instruct	Devstral Medium
Intelligence Index Composite reasoning & knowledge score (0-100)	76	52
Provider	Alibaba	Mistral AI
Input Price Cost per million input tokens	$0.00 / 1M	$0.80 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$2.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.54 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	140 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	N/A	51.5%
GPQA Diamond Graduate-level reasoning	N/A	62%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.20 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Qwen3-Coder 30B-A3B Instruct

1.0x cheaper monthly runtime

Speed

Qwen3-Coder 30B-A3B Instruct

+87% faster generation

Context

Qwen3-Coder 30B-A3B Instruct

262.1K tokens window

Skor Benchmark Terverifikasi

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

Qwen3-Coder 30B-A3B InstructDevstral Medium

SWE-bench Verified (Autonomous Coding)

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

N/Avs51.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs62%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs80.5%

LMSYS Chatbot Arena Elo

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

1285ptsvs1330pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs45%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

63%vsN/A

AIME (Advanced Math Competition)

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

N/Avs72%

Long-Context Reasoning

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

N/Avs78%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs75 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

Qwen3-Coder 30B-A3B Instruct0.28s

Devstral Medium0.50s

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

Qwen3-Coder 30B-A3B Instruct3.85s

Devstral Medium7.17s

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)

Qwen3-Coder 30B-A3B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Devstral Medium Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

Qwen3-Coder 30B-A3B Instruct saves approximately $3.66 / month (100%) under this workload.1.0x Savings

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 74% · Arena Elo 1285)
Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 280 ms
Bobot terbuka (30B total (3B MoE)) 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

Devstral Medium · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 51.5% · GPQA Diamond 62%)
Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 500 ms
Skema harga API kompetitif (tarif $0.8/$2.4 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
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	Qwen3-Coder 30B-A3B Instruct	Devstral Medium
Cost per Task Estimated USD to finish one task	$0.00	$0.55
Output Tokens / Task Tokens generated per task	15K	38K
Reasoning Tokens / Task Thinking tokens burned per task	0K	18K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3-Coder 30B-A3B Instruct	Devstral Medium
Official API Model ID (SDK)	qwen3-coder-30b-a3b-instruct	devstral-medium-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Mistral AI REST API (OpenAI-Compatible)
Release Date	2025-04	2025-07
Knowledge Cutoff	March 2025	May 2025
Architecture	Compact Sparse Mixture-of-Experts Transformer	High-Capacity Agentic Code Transformer
Parameters	30B total (3B active MoE)	Confidential (~70B)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.8/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.2/1M tokens
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)	API Prompt Engineering & RAG Distillation
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) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Mistral AI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text
Output Modalities	text	text

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium: Jawaban Cepat (FAQ)

Which is more intelligent, Qwen3-Coder 30B-A3B Instruct or Devstral Medium?

Qwen3-Coder 30B-A3B Instruct is more intelligent. It scores 76 compared with 52 on the composite Intelligence Index.

Which model is faster?

Qwen3-Coder 30B-A3B Instruct is faster, generating 140 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 30B-A3B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3-Coder 30B-A3B Instruct has lower latency, with a time to first token of 0.28s compared with 0.50s.

Which model has a larger context window?

Qwen3-Coder 30B-A3B Instruct has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Kesimpulan

Baik Qwen3-Coder 30B-A3B Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Devstral Medium (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3-Coder 30B-A3B Instruct unggul dalam penalaran dan pemrograman, Qwen3-Coder 30B-A3B Instruct memberikan kecepatan generasi token lebih tinggi, dan Qwen3-Coder 30B-A3B Instruct lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3-Coder 30B-A3B Instruct atau Devstral Medium?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Coder 30B-A3B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3-Coder 30B-A3B Instruct vs Devstral Medium?+

Qwen3-Coder 30B-A3B Instruct mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Devstral Medium sebesar $0.80 / $2.40. Secara keseluruhan, Qwen3-Coder 30B-A3B Instruct lebih hemat biaya.

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

Qwen3-Coder 30B-A3B Instruct mendukung konteks 262.1K token dibandingkan 128K token pada Devstral Medium, sementara Qwen3-Coder 30B-A3B Instruct 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)

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium

Ganti & Bandingkan Model Apa Saja

Qwen3-Coder 30B-A3B Instruct

Devstral Medium

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Devstral Medium · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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