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Claude Sonnet 4.6 vs Step 5 Preview

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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Claude Sonnet 4.6 scores higher, Step 5 Preview costs less — it depends on your workload.

Claude Sonnet 4.6 is ahead by 3.1 points overall, and Step 5 Preview lists 4.2× cheaper per blended million tokens. Whether 3.1 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Step 5 Preview has the lower sticker price, but Claude Sonnet 4.6 earns each point of capability for less — $1.78 against $4.08 — because per-token rates do not predict how many tokens a model actually spends on a task.

anthropic

Claude Sonnet 4.6

Blended / 1M
$6.00
Context
1M
Released
Feb 17, 2026
Overall score
65.1
reasoningtool callingimage inputfile inputprompt caching

stepfun

Step 5 Preview

Blended / 1M
$1.43
Context
1.0M
Released
Oct 8, 2026
Overall score
62.0
reasoningtool callingimage inputvideo inputprompt caching

Specs and pricing

MetricClaude Sonnet 4.6Step 5 Preview
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

65.1win62.0
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$1.78win$4.08
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$6.00$1.43win
Input price / 1M$3.00$1.00win
Output price / 1M$15.00$2.70win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.300$0.100win
Context window1M1.0M
Max output tokens128Kwin64K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Sonnet 4.6 on top, Step 5 Preview below, both out of 100.

Coding
66.6
85.6
Reasoning
86.4
83.8
Data analysis
76.1
63.5

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.

WorkloadClaude Sonnet 4.6Step 5 Preview
Support chatbot

1.2K in / 400 out × 200K requests

$1,726/mo$391.20/mo
RAG assistant

8K in / 600 out × 100K requests

$2,220/mo$602.00/mo
Coding agent

40K in / 4K out × 20K requests

$2,088/mo$512.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$3,990/mo$1,158/mo
Bulk classification

500 in / 20 out × 5M requests

$7,650/mo$2,320/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Claude Sonnet 4.6

Lowest measured cost per point of capability at $1.78 per point — the gap compounds with every request.

Quality matters more than the bill

Claude Sonnet 4.6

Highest overall LiveBench score of the two at 65.1.

The workload is coding or agentic work

Step 5 Preview

Leads on coding — 85.6 against 66.6.

Claude Sonnet 4.6 vs Step 5 Preview FAQ

Which is better, Claude Sonnet 4.6 or Step 5 Preview?

Claude Sonnet 4.6 scores higher, Step 5 Preview costs less — it depends on your workload. Claude Sonnet 4.6 is ahead by 3.1 points overall, and Step 5 Preview lists 4.2× cheaper per blended million tokens. Whether 3.1 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Step 5 Preview has the lower sticker price, but Claude Sonnet 4.6 earns each point of capability for less — $1.78 against $4.08 — because per-token rates do not predict how many tokens a model actually spends on a task.

Is Claude Sonnet 4.6 cheaper than Step 5 Preview?

Step 5 Preview is cheaper. On a 3:1 input:output blend, Claude Sonnet 4.6 lists at $6.00 per million tokens and Step 5 Preview at $1.43 — Step 5 Preview is 4.2× cheaper. Input and output are priced separately — Claude Sonnet 4.6 charges $3.00 in and $15.00 out, Step 5 Preview charges $1.00 and $2.70 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Sonnet 4.6 vs Step 5 Preview: which scores higher on benchmarks?

Claude Sonnet 4.6 scores 65.1 and Step 5 Preview scores 62.0 overall on LiveBench, the mean of its seven categories. That is a 3.1-point lead for Claude Sonnet 4.6. 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 Sonnet 4.6 or Step 5 Preview?

Claude Sonnet 4.6. 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 Sonnet 4.6 works out at $1.78 per point and Step 5 Preview at $4.08.

Does Claude Sonnet 4.6 or Step 5 Preview have a bigger context window?

They are effectively the same — 1M for Claude Sonnet 4.6 and 1.0M for Step 5 Preview.

Do Claude Sonnet 4.6 and Step 5 Preview support prompt caching?

Both publish a cached-input rate: $0.300 per million for Claude Sonnet 4.6 and $0.100 for Step 5 Preview, against full input rates of $3.00 and $1.00. 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 2026-09-02, 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.