// head_to_head
Claude Haiku 5.5 vs DeepSeek V4 Flash 0423
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Claude Haiku 5.5 wins outright — it scores higher and costs less.
Claude Haiku 5.5 leads by 6.6 points overall while listing 1.7× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Claude Haiku 5.5 has the lower sticker price, but DeepSeek V4 Flash 0423 earns each point of capability for less — $0.0083 against $0.0100 — because per-token rates do not predict how many tokens a model actually spends on a task.
anthropic
Claude Haiku 5.5
- Blended / 1M
- $0.200
- Context
- 1M
- Released
- Oct 7, 2026
- Overall score
- 72.1
deepseek
DeepSeek V4 Flash 0423
- Blended / 1M
- $0.343
- Context
- 1.0M
- Released
- Apr 24, 2026
- Overall score
- 65.5
Specs and pricing
| Metric | Claude Haiku 5.5 | DeepSeek V4 Flash 0423 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 72.1win | 65.5 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0100 | $0.0083win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200win | $0.343 |
| Input price / 1M | $0.100 | $0.030win |
| Output price / 1M | $0.500win | $1.28 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010win | $0.030 |
| Context window | 1M | 1.0M |
| Max output tokens | 128K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Haiku 5.5 on top, DeepSeek V4 Flash 0423 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Claude Haiku 5.5 | DeepSeek V4 Flash 0423 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $109.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $100.80/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $126.40/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $126.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $203.00/mo |
Which should you pick?
You are running this at volume
DeepSeek V4 Flash 0423
Lowest measured cost per point of capability at $0.0083 per point — the gap compounds with every request.
Quality matters more than the bill
Claude Haiku 5.5
Highest overall LiveBench score of the two at 72.1.
The workload is coding or agentic work
Claude Haiku 5.5
Leads on agentic coding — 51.4 against 37.6.
Claude Haiku 5.5 vs DeepSeek V4 Flash 0423 FAQ
Which is better, Claude Haiku 5.5 or DeepSeek V4 Flash 0423?
Claude Haiku 5.5 wins outright — it scores higher and costs less. Claude Haiku 5.5 leads by 6.6 points overall while listing 1.7× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Claude Haiku 5.5 has the lower sticker price, but DeepSeek V4 Flash 0423 earns each point of capability for less — $0.0083 against $0.0100 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Claude Haiku 5.5 cheaper than DeepSeek V4 Flash 0423?
Claude Haiku 5.5 is cheaper. On a 3:1 input:output blend, Claude Haiku 5.5 lists at $0.200 per million tokens and DeepSeek V4 Flash 0423 at $0.343 — Claude Haiku 5.5 is 1.7× cheaper. Input and output are priced separately — Claude Haiku 5.5 charges $0.100 in and $0.500 out, DeepSeek V4 Flash 0423 charges $0.030 and $1.28 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Claude Haiku 5.5 vs DeepSeek V4 Flash 0423: which scores higher on benchmarks?
Claude Haiku 5.5 scores 72.1 and DeepSeek V4 Flash 0423 scores 65.5 overall on LiveBench, the mean of its seven categories. That is a 6.6-point lead for Claude Haiku 5.5. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Claude Haiku 5.5 or DeepSeek V4 Flash 0423?
DeepSeek V4 Flash 0423. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Claude Haiku 5.5 works out at $0.0100 per point and DeepSeek V4 Flash 0423 at $0.0083.
Does Claude Haiku 5.5 or DeepSeek V4 Flash 0423 have a bigger context window?
They are effectively the same — 1M for Claude Haiku 5.5 and 1.0M for DeepSeek V4 Flash 0423.
Do Claude Haiku 5.5 and DeepSeek V4 Flash 0423 support prompt caching?
Both publish a cached-input rate: $0.010 per million for Claude Haiku 5.5 and $0.030 for DeepSeek V4 Flash 0423, against full input rates of $0.100 and $0.030. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.