GetInfoUs
Head-to-Head Model Comparison

Pixtral Large vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
Bagikan
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Pixtral Large vs Aya Expanse 32B: Benchmarks, Pricing & Speed Comparison

Pixtral Large dari Mistral AI dan Aya Expanse 32B dari Cohere adalah dua model AI unggulan. Pixtral Large menawarkan jendela konteks 128K token dengan harga $2.00/$6.00 per 1 juta token, sedangkan Aya Expanse 32B menyediakan konteks 128K token seharga $0.25/$0.50 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

Ganti & Bandingkan Model Apa Saja

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
Mistral AI

Pixtral Large

AI Multimodal Asli

Pixtral Large dari Mistral AI adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 128K, kecepatan 70 token/detik, indeks kecerdasan 48/100, serta tarif

Intelligence Index48 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.5%

Cohere

Aya Expanse 32B

Model Multibahasa Global

Aya Expanse 32B dari Cohere adalah model AI yang dirancang untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, menghadirkan jendela konteks 128K, kecepatan 85 token/detik, indeks kecerdasan 44/100, serta tarif $0.25

Intelligence Index44 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

Perbedaan Utama Sekilas

  • Aya Expanse 32B is 10.8x cheaper for API usage (input + output tokens combined).
  • Aya Expanse 32B is faster at 85 tok/s vs 70 tok/s.
  • Pixtral Large leads on SWE-bench coding (38.5% vs 33.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeaturePixtral LargeAya Expanse 32B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4844
Provider
Mistral AICohere
Input Price
Cost per million input tokens
$2.00 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$6.00 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.70 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
70 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
38.5%33.5%
GPQA Diamond
Graduate-level reasoning
55.4%50%
Cached Input Price
Prompt caching discount per 1M tokens
$1.00 / 1M$0.06 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
Pixtral Large
+5.0% SWE-bench lead
Cost
Aya Expanse 32B
10.8x cheaper monthly runtime
Speed
Aya Expanse 32B
+21% faster generation
Context
Tied
128K tokens window

Skor Benchmark Terverifikasi

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

Pixtral LargeAya Expanse 32B
SWE-bench Verified (Autonomous Coding)

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

38.5%vs33.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs50%
MMLU-Pro (Multi-task Language Understanding)

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

78%vs73%
LMSYS Chatbot Arena Elo

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

1295ptsvs1265pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

34%vsN/A
AIME (Advanced Math Competition)

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

65%vsN/A
Long-Context Reasoning

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

75%vs72%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs85 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
Pixtral Large0.55s
Aya Expanse 32B0.44s
Est. End-to-End Response (500 tokens)TTFT + generation time
Pixtral Large7.69s
Aya Expanse 32B6.32s

Simulator Biaya Token Interaktif

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Pixtral Large Monthly API Bill
$9.90 / month
$2/1M in · $6/1M out
Aya Expanse 32B Monthly API Bill
$0.92 / month
$0.25/1M in · $0.5/1M out
Aya Expanse 32B saves approximately $8.98 / month (91%) under this workload.10.8x Savings

Pixtral Large · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 38.5% · GPQA Diamond 55.4%)
  • Kecepatan generasi tinggi 70 token/detik dengan latensi token pertama (TTFT) hanya 550 ms
  • Bobot terbuka (124B) 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
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

Aya Expanse 32B · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 33.5% · GPQA Diamond 50%)
  • Kecepatan generasi tinggi 85 token/detik dengan latensi token pertama (TTFT) hanya 440 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

Token Use & Task Economics

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

MetricPixtral LargeAya Expanse 32B
Cost per Task
Estimated USD to finish one task
$0.65$0.18
Output Tokens / Task
Tokens generated per task
35K28K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeaturePixtral LargeAya Expanse 32B
Official API Model ID (SDK)pixtral-large-latestaya-expanse-32b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2024-112024-10
Knowledge CutoffOctober 2024August 2024
ArchitectureFrontier Vision-Language TransformerHigh-Capacity Multilingual Transformer
Parameters124B32B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 128K windowFlat rate ($0.25/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $1/1M tokensCached input at $0.06/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 77 GB VRAM (4-bit Quantized) · 267 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Pixtral Large vs Aya Expanse 32B: Jawaban Cepat (FAQ)

Which is more intelligent, Pixtral Large or Aya Expanse 32B?

Pixtral Large is more intelligent. It scores 48 compared with 44 on the composite Intelligence Index.

Which model is faster?

Aya Expanse 32B is faster, generating 85 tokens per second compared with 70 tokens per second.

Which model is cheaper to run?

Aya Expanse 32B is cheaper at $0.14 per 1M tokens blended, compared with $1.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Expanse 32B has lower latency, with a time to first token of 0.44s compared with 0.55s.

Kesimpulan

Baik Pixtral Large (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Aya Expanse 32B (Sangat cocok untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Pixtral Large unggul dalam penalaran dan pemrograman, Aya Expanse 32B memberikan kecepatan generasi token lebih tinggi, dan Aya Expanse 32B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Pixtral Large atau Aya Expanse 32B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Pixtral Large memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Pixtral Large vs Aya Expanse 32B?+

Pixtral Large mengenakan biaya $2.00 / $6.00 per 1M token input/output, sedangkan Aya Expanse 32B sebesar $0.25 / $0.50. Secara keseluruhan, Aya Expanse 32B lebih hemat biaya.

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

Pixtral Large mendukung konteks 128K token dibandingkan 128K token pada Aya Expanse 32B, sementara Aya Expanse 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Sumber resmi: Dokumentasi API resmi Mistral AI & Cohere serta evaluasi benchmark independen (2026).