Full Budget Model Comparison
Both models sit at the ultra-budget tier. Here's how they stack up against the full field:
| Model | Input/1M | Output/1M | Context | Blended* |
|---|---|---|---|---|
| Llama 4 Scout | $0.18 | $0.34 | 10M | $0.18 |
| DeepSeek V4 Flash | $0.14 | $0.28 | 1M | $0.19 |
| Gemini 2.5 Flash-Lite | $0.10 | $0.40 | 1M | $0.20 |
| GPT-oss 20B | $0.08 | $0.35 | 128K | $0.17 |
| GPT-4o mini | $0.15 | $0.60 | 128K | $0.30 |
| DeepSeek V4 Pro | $0.44 | $0.87 | 1M | $0.55 |
| Mistral Small 4 | $0.10 | $0.30 | 128K | $0.30 |
| Claude Haiku 4.5 | $1.00 | $5.00 | 200K | $1.90 |
*Blended cost assumes a 3:1 input-to-output ratio, typical for chat workloads.
Both are under $0.20 blended — but the details matter
Llama 4 Scout edges out on input price ($0.18 vs $0.14) and has a 10x larger context window (10M vs 1M). DeepSeek V4 Flash wins on output price ($0.28 vs $0.34) — an 18% cheaper output. For output-heavy workloads like code generation, DeepSeek's lower output cost compounds fast.
Cost Scenario 1: Chatbot (1M tokens/day, 60/40 split)
A production chatbot processing 1M tokens daily with a 60% input / 40% output split (18M input + 12M output per month):
| Model | Input/mo | Output/mo | Total/mo | vs Llama 4 Scout |
|---|---|---|---|---|
| Llama 4 Scout | $1.98 | $4.08 | $6.06 | — |
| DeepSeek V4 Flash | $2.52 | $3.36 | $5.88 | -3% |
| Gemini 2.5 Flash-Lite | $1.80 | $4.80 | $6.60 | +9% |
| GPT-4o mini | $2.70 | $7.20 | $9.90 | +63% |
| Mistral Small 4 | $2.70 | $7.20 | $9.90 | +63% |
| Claude Haiku 4.5 | $18.00 | $60.00 | $78.00 | +1,188% |
Winner: DeepSeek V4 Flash — $5.88/month vs Llama 4 Scout's $6.06. The output price difference (18% cheaper) overcomes Scout's input advantage. But both are under $6/month — a $72/year chatbot at 1M tokens/day. That's 96% cheaper than Claude Haiku.
Cost Scenario 2: Long-Context Document Analysis (500 requests/day, 50K input + 2K output)
Processing large documents — legal contracts, research papers, codebases — with 50K input tokens per request (750M input + 30M output per month):
| Model | Input/mo | Output/mo | Total/mo | vs Llama 4 Scout |
|---|---|---|---|---|
| Llama 4 Scout | $82.50 | $10.20 | $92.70 | — |
| DeepSeek V4 Flash | $105.00 | $8.40 | $113.40 | +22% |
| Gemini 2.5 Flash-Lite | $75.00 | $12.00 | $87.00 | -6% |
| DeepSeek V4 Pro | $330.00 | $26.10 | $356.10 | +284% |
Winner: Gemini 2.5 Flash-Lite at $87/month, but Llama 4 Scout (1M context) window — 10x DeepSeek's 1M. For documents over 1M tokens, Llama 4 Scout is the only option that doesn't require chunking. DeepSeek V4 Flash at $113.40 is 22% more expensive due to its higher input price at this volume.
Cost Scenario 3: High-Volume Classification (50K requests/day, 200 input + 50 output)
Sentiment analysis, content moderation, or intent classification at massive scale (300M input + 75M output per month):
| Model | Input/mo | Output/mo | Total/mo | vs Llama 4 Scout |
|---|---|---|---|---|
| Llama 4 Scout | $33.00 | $25.50 | $58.50 | — |
| DeepSeek V4 Flash | $42.00 | $21.00 | $63.00 | +8% |
| Gemini 2.5 Flash-Lite | $30.00 | $30.00 | $60.00 | +3% |
| GPT-4o mini | $45.00 | $45.00 | $90.00 | +54% |
| Mistral Small 4 | $45.00 | $45.00 | $90.00 | +54% |
Winner: Llama 4 Scout at $58.50/month. At high volume with short outputs, Scout's lower input price ($0.18 vs $0.14) saves $9/month compared to DeepSeek. Both crush GPT-4o mini and Mistral Small 4 by 35%.
Context Window: 10M vs 1M
Llama 4 Scout's 10M token context window is the largest available via API — 10x DeepSeek V4 Flash's 1M. This is a game-changer for specific workloads:
- Full codebase analysis: Scout can ingest an entire large repository (1M tokens ~ 7.5M words) in a single call. DeepSeek requires chunking at ~750K words.
- Multi-document processing: Analyze dozens of contracts, research papers, or reports simultaneously without splitting.
- Long conversation memory: Scout retains massive conversation history, reducing the need for context management in long-running agents.
- RAG with large retrieval sets: Fit more retrieved chunks in context, improving answer quality for complex queries.
However, 1M context comes with trade-offs:
- Latency: Processing 1M tokens takes significantly longer than 1M. For short requests, this doesn't matter.
- Cost at scale: If you're sending 50K input tokens per request, the per-token cost difference adds up fast — DeepSeek's lower output price may offset Scout's input advantage.
- Quality degradation: Some models show reduced accuracy at very long contexts ("lost in the middle" effect). Both Scout and Flash handle long context well, but quality can vary.
Quality Comparison: Where Each Model Excels
Llama 4 Scout: The open-source workhorse
Meta's Llama 4 Scout is the latest in the Llama family, optimized for general-purpose tasks with excellent instruction following. It inherits Llama's strengths in multilingual support, reasoning, and code generation. Available via Together.ai with dedicated inference — meaning consistent performance without serverless cold starts.
DeepSeek V4 Flash: The coding champion
DeepSeek has earned a strong reputation for code generation and mathematical reasoning. V4 Flash continues this tradition with excellent coding benchmarks, structured output, and technical Q&A. It's the go-to budget model for developer tools and coding assistants.
| Capability | Llama 4 Scout | DeepSeek V4 Flash |
|---|---|---|
| Code generation | Very Good | Excellent |
| Math & reasoning | Excellent | Excellent |
| Natural conversation | Excellent | Good |
| Instruction following | Excellent | Good |
| Multilingual support | Excellent | Good |
| Structured output | Good | Excellent |
| Long context handling | Excellent (10M) | Very Good (1M) |
| Self-hosting option | Yes (open weights) | No (API only) |
Provider & Hosting Differences
These models have different availability models that affect your decision:
| Aspect | Llama 4 Scout | DeepSeek V4 Flash |
|---|---|---|
| Provider | Together.ai | DeepSeek |
| Model type | Open weights (Meta) | Proprietary (open weights for V3) |
| Self-hosting | Yes — run on your own GPU cluster | No — API only |
| Inference type | Dedicated (not serverless) | Serverless |
| Data privacy | Full control with self-hosting | Data sent to DeepSeek servers |
| EU data sovereignty | Yes (self-host or Together.ai EU) | Depends on DeepSeek infrastructure |
Self-hosting changes the math entirely
If you're running Llama 4 Scout on your own infrastructure, the per-token API cost becomes irrelevant. At high utilization (>80% GPU uptime), self-hosting Llama 4 Scout can be 50-70% cheaper than any API — including DeepSeek. The break-even point depends on your GPU costs and utilization rate. For teams with existing GPU infrastructure, Llama 4 Scout is the clear winner.
When to Choose Llama 4 Scout
- Massive context needs: When you need to process documents, codebases, or conversations that exceed 1M tokens
- Self-hosting: If you have GPU infrastructure and want to eliminate API vendor lock-in
- Data privacy: When you can't send data to external APIs (healthcare, finance, government)
- Multilingual applications: Broader and more reliable multilingual support than DeepSeek
- General-purpose chat: Better natural language quality for conversational AI
- EU data sovereignty: Self-host or use Together.ai's EU infrastructure
- High-volume input-heavy tasks: Classification, tagging, and analysis where input cost dominates
When to Choose DeepSeek V4 Flash
- Code generation: Best-in-class coding quality at this price point
- Output-heavy workloads: 18% cheaper output means real savings at scale
- Structured output: JSON extraction, function calling, data formatting
- Serverless simplicity: No infrastructure to manage — just API calls
- Math and reasoning: Excellent performance on technical and mathematical tasks
- 1M context is enough: For most workloads, 1M tokens is more than sufficient
- Drop-in replacement: If you're already using DeepSeek V3, V4 Flash is a seamless upgrade
The Bottom Line
Two ultra-budget champions, different strengths
At under $0.20 blended cost per million tokens, both Llama 4 Scout and DeepSeek V4 Flash are 95%+ cheaper than premium models like Claude Haiku ($1.90 blended). The choice comes down to your workload:
Choose Llama 4 Scout if you need massive context (1M tokens), want to self-host, care about data privacy, or need multilingual support. At $0.18 input, it's the cheapest way to process enormous amounts of text.
Choose DeepSeek V4 Flash if you need best-in-class code generation, output-heavy workloads, or serverless simplicity. At $0.28 output, it's the cheapest way to generate high-quality code and structured content.
The smart move? Use both. Route coding tasks to DeepSeek, long-context analysis to Llama 4 Scout, and keep general chat on either. At these prices, a multi-model pipeline costs under $10/month for most workloads.
Calculate your exact costs: Plug your real workload into our free calculator and see exactly what each model would cost you — down to the penny.
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