Two open-weights models, similar prices, and the same underlying API infrastructure. DeepSeek V3 wins 10 of 15 tasks against Llama 4 Maverick on coding, reasoning, and instruction-following. The margin is larger than the vendor benchmarks suggest, but Llama 4 has two genuine edges worth knowing.
Why these two models
Both are MoE (Mixture of Experts) architectures that deliver near-frontier quality at prices under $1 per million input tokens. DeepSeek V3 activates 37 billion parameters per token from a 671B total pool; Llama 4 Maverick activates 17B from 128 experts. For the cost implications of that architecture, the MoE explainer breaks down why active-parameter count drives your API bill, not total parameter count.
The two models sit at different ends of a trade-off: DeepSeek V3 at $0.20 input / $0.80 output per million tokens with a 128K context window; Llama 4 Maverick at $0.15 input / $0.60 output per million tokens with a 1M context window. On paper, Llama is cheaper and longer. In practice, quality is the third variable.
The test setup
Fifteen prompts across three categories:
Coding (6 prompts): debugging a binary search with two pointer bugs, refactoring callback-heavy Node.js to async/await, writing a PostgreSQL window function query for monthly customer rankings, implementing an O(1) LRU cache in Python, designing a sliding window rate limiter as Express middleware with Redis, and adding exponential retry plus a three-state circuit breaker to an HTTP client.
Reasoning (4 prompts): a five-person logic deduction puzzle with seven constraints, Bayesian probability across three servers with a fourth added mid-problem, identifying a race condition in async Node.js and proposing a fix, and a startup architecture recommendation with specific reversal criteria.
Instruction-following (5 prompts): parsing unstructured text into JSON with a strict schema, writing marketing copy under seven simultaneous hard constraints, translating Python to idiomatic Go, generating a pipe-delimited markdown comparison table with exact "Winner" values, and identifying the right PostgreSQL indexes for a 50M-row table.
Each prompt ran through both models. The judge evaluated each pair twice with positions swapped (DeepSeek as A then B, Llama as B then A) to eliminate ordering bias. A decisive win required both evaluations to agree. Disagreement counted as a tie.
Win breakdown
| Category | DeepSeek V3 wins | Llama 4 Maverick wins | Ties | Errors |
|---|---|---|---|---|
| Coding (6) | 5 | 1 | 0 | 0 |
| Reasoning (4) | 2 | 0 | 1 | 1 |
| Instruction-following (5) | 3 | 1 | 1 | 0 |
| Total (15) | 10 | 2 | 2 | 1 |
The error was the five-person logic puzzle, which timed out at the 45-second evaluation window. Both models produced plausible chains of reasoning but the judge's combined evaluation stalled.
Average response latency: DeepSeek V3 at 19.8 seconds, Llama 4 Maverick at 13.2 seconds. Llama is 50% faster, which matters for synchronous user-facing requests.
Three representative prompts
1. Startup architecture recommendation (DeepSeek wins)
The prompt asked for a concrete monolith vs microservices recommendation for a three-person team with 50 beta users, one specific reversal scenario, and measurable milestones.
DeepSeek V3 recommended a monolith immediately, gave three concrete reversal conditions (scaling bottlenecks in a specific subsystem, team growing past five engineers, explicit enterprise SLA requirements), and listed milestones in concrete numbers. Llama 4 Maverick reached the same recommendation but its response truncated mid-sentence during step 6 of its analysis, before it provided either the reversal scenario or the milestones the prompt required.
The judge's forward-pass assessment:
"Assistant A directly addresses all three requirements with concrete, actionable advice. It gives a clear monolith recommendation with solid reasoning (team size, complexity overhead, cost), identifies a specific reversal scenario (scaling bottlenecks in subsystems), and provides measurable technical milestones (>10K users, >5 engineers, enterprise SLA requirements, technical debt indicators). Assistant B starts with the same correct recommendation and sound reasoning, but fails to complete the response — it cuts off mid-sentence in 'Step 6: Identify the Scenar' without providing the requested reversal scenario or technical milestones."
2. O(1) LRU cache in Python (Llama 4 Maverick wins)
Llama won this one by delivering a working solution. The prompt asked for a complete O(1) LRU cache implementation with comments explaining why each data structure choice achieves O(1), edge-case handling, and eviction examples.
Llama used Python's OrderedDict and returned a complete, runnable implementation covering all edge cases: capacity=1, cache misses, updating an existing key. DeepSeek V3 reached for a custom doubly-linked list approach (more educational for explaining why each pointer operation is O(1)), but its response truncated mid-method and left the implementation incomplete.
The judge's assessment:
"Assistant A provides a more comprehensive implementation using a custom doubly-linked list with hash map approach. The comments thoroughly explain why each data structure choice achieves O(1) complexity, and the implementation correctly handles all edge cases. However, the response appears to be cut off mid-sentence in the
getmethod, making it incomplete. Assistant B offers a clean, working implementation using Python's OrderedDict, which is a valid and elegant solution that achieves the required O(1) complexity. The code is complete and functional, with good comments explaining the approach."
3. Retry logic and circuit breaker (DeepSeek wins)
Both models truncated on this one. Adding exponential backoff, three circuit-breaker state transitions, 429 Retry-After handling, and structured logging to a bare fetch wrapper pushes well past 2,048 output tokens. DeepSeek got further before cutting off.
The judge noted:
"Assistant A correctly implements the core retry logic with exponential backoff (1s, 2s, 4s), properly handles the distinction between retryable errors (5xx, network errors, 429) and non-retryable ones (4xx except 429), and includes logic for respecting the Retry-After header. The circuit breaker implementation tracks failures and implements the state transitions as specified. However, the code appears to be cut off at the end... Both responses are incomplete due to truncation, but Assistant A gets much closer to a working solution that addresses the specific requirements, while Assistant B provides more of a tutorial introduction without the actual implementation."
The 2K token cap is a real constraint for complex code generation tasks. If you're building a system where full implementations matter, the models' outputs at higher token budgets will likely narrow this gap.
Pricing and access
DeepSeek V3 is available through OpenRouter as deepseek/deepseek-chat at $0.20 input / $0.80 output per million tokens (128K context). One note: this model ID is scheduled for deprecation on July 24, 2026, when it will transition to V4 identifiers. If you pin the model ID in your code, update it before then.
DeepSeek V3 is open-weights under MIT license. Self-hosting the full 671B model requires multi-node GPU infrastructure (the on-prem open-source LLM guide covers what that hardware actually costs).
Llama 4 Maverick is available through OpenRouter as meta-llama/llama-4-maverick at $0.15 input / $0.60 output per million tokens. Its 1M context window is its strongest differentiator: useful for long document analysis, large codebases, or any task where DeepSeek V3's 128K limit creates a hard ceiling. Commercial use is permitted for organizations below 700 million monthly active users.
Verdict
DeepSeek V3 is the stronger general-purpose model at this price point. It won on coding depth, reasoning completeness, and multi-constraint instruction-following, losing primarily when its longer responses hit the token output ceiling. If you're running at 2K+ output tokens, raise the limit and DeepSeek V3's win rate on complex code tasks would likely improve.
Use DeepSeek V3 when quality on complex reasoning or multi-constraint generation matters more than response latency.
Use Llama 4 Maverick when: you need a 1M context window, latency on synchronous calls is critical, or you want the cheapest possible open-weights API option.
Neither model makes sense if you need consistent performance on tasks that reliably exceed 2K output tokens without a higher token cap configured. Our SQL generation benchmark shows how both compare against closed models on structured output tasks that stay within shorter response lengths.
Run your own prompts against both models at LLMTest.
How this was tested
- Judge model:
anthropic/claude-sonnet-4(LLMTest's standard benchmark judge, per the methodology documentation) - Prompts: 15 across coding, reasoning, and instruction-following
- Position-swap methodology: each prompt evaluated forward and reverse; decisive win requires both evaluations to agree
- Output token cap: 2,048 per response (long code outputs can hit this limit)
- Total runner cost: $0.22 across 15 prompts, 2 models, and 30 judge evaluation pairs