Head-to-Head Model Comparison

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct

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.

Devstral 2Profile

Qwen3-Coder 480B-A35B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Devstral 2

Model Agen Otonom & Alat

Devstral 2 dari Mistral AI adalah model AI yang dirancang untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, menghadirkan jendela konteks 262K, kecepatan 70 token/detik, indeks kecerdasan 56/100, s

Intelligence Index56 / 100

Input Token:

$1.50 / 1M

Output Token:

$4.50 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

58.2%

Alibaba

Qwen3-Coder 480B-A35B Instruct

Model Dasar Bobot Terbuka

Qwen3-Coder 480B-A35B 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 52 token/detik, indeks kecerdasan 86/100,

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

56.5%

Perbedaan Utama Sekilas

Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
Devstral 2 is faster at 70 tok/s vs 52 tok/s.
Devstral 2 leads on SWE-bench coding (58.2% vs 56.5%).

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

Feature	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	56	86
Provider	Mistral AI	Alibaba
Input Price Cost per million input tokens	$1.50 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$4.50 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.99 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	66K	16K
Throughput Speed Tokens generated per second	70 tok/s	52 tok/s
SWE-bench Verified Real-world software engineering	58.2%	56.5%
GPQA Diamond Graduate-level reasoning	66.5%	70%
Cached Input Price Prompt caching discount per 1M tokens	$0.35 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Devstral 2

+1.7% SWE-bench lead

Cost

Qwen3-Coder 480B-A35B Instruct

1.0x cheaper monthly runtime

Speed

Devstral 2

+35% faster generation

Context

Tied

262.1K tokens window

Skor Benchmark Terverifikasi

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

Devstral 2Qwen3-Coder 480B-A35B Instruct

SWE-bench Verified (Autonomous Coding)

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

58.2%vs56.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

66.5%vs70%

MMLU-Pro (Multi-task Language Understanding)

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

83.5%vs83%

LMSYS Chatbot Arena Elo

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

1365ptsvs1355pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

52%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs73%

AIME (Advanced Math Competition)

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

78%vsN/A

Long-Context Reasoning

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

82%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs52 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

Devstral 20.52s

Qwen3-Coder 480B-A35B Instruct0.76s

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

Devstral 27.66s

Qwen3-Coder 480B-A35B Instruct10.38s

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)

Devstral 2 Monthly API Bill

$6.82 / month

$1.5/1M in · $4.5/1M out

Qwen3-Coder 480B-A35B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3-Coder 480B-A35B Instruct saves approximately $6.82 / month (100%) under this workload.1.0x Savings

Devstral 2 · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 58.2% · GPQA Diamond 66.5%)
Kecepatan generasi tinggi 70 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
Bobot terbuka (123B) 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

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 56.5% · GPQA Diamond 70%)
Kecepatan generasi tinggi 52 token/detik dengan latensi token pertama (TTFT) hanya 760 ms
Bobot terbuka (480B total (35B 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

Token Use & Task Economics

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

Metric	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Cost per Task Estimated USD to finish one task	$0.65	$0.00
Output Tokens / Task Tokens generated per task	42K	25K
Reasoning Tokens / Task Thinking tokens burned per task	20K	9K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Official API Model ID (SDK)	devstral-2-latest	qwen3-coder-480b-a35b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-04
Knowledge Cutoff	October 2025	March 2025
Architecture	Advanced Agentic Code Transformer	Code-Specialized Sparse Mixture-of-Experts Transformer
Parameters	123B	480B total (35B active MoE)
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 ($1.5/1M in) across full 262K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.35/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 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

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Jawaban Cepat (FAQ)

Which is more intelligent, Devstral 2 or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 56 on the composite Intelligence Index.

Which model is faster?

Devstral 2 is faster, generating 70 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 480B-A35B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.99 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Devstral 2 has lower latency, with a time to first token of 0.52s compared with 0.76s.

Kesimpulan

Baik Devstral 2 (Sangat cocok untuk alur kerja agen otonom, pemanggilan alat (tool calling), dan otomatisasi multi-langkah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Qwen3-Coder 480B-A35B Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3-Coder 480B-A35B Instruct unggul dalam penalaran dan pemrograman, Devstral 2 memberikan kecepatan generasi token lebih tinggi, dan Qwen3-Coder 480B-A35B Instruct lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Devstral 2 atau Qwen3-Coder 480B-A35B Instruct?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Coder 480B-A35B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Devstral 2 vs Qwen3-Coder 480B-A35B Instruct?+

Devstral 2 mengenakan biaya $1.50 / $4.50 per 1M token input/output, sedangkan Qwen3-Coder 480B-A35B Instruct sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3-Coder 480B-A35B Instruct lebih hemat biaya.

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

Devstral 2 mendukung konteks 262.1K token dibandingkan 262.1K token pada Qwen3-Coder 480B-A35B Instruct, sementara Devstral 2 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)

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct

Ganti & Bandingkan Model Apa Saja

Devstral 2

Qwen3-Coder 480B-A35B Instruct

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Devstral 2 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

Related reading