---
title: "Anthropic: Claude Sonnet 5 — price, capability and what beats it · Undominated.ai"
canonical: https://undominated.ai/models/anthropic__claude-sonnet-5/
description: "Anthropic: Claude Sonnet 5: $2.00/M in, $10.00/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper."
---

# Anthropic: Claude Sonnet 5 — price, capability and what beats it · Undominated.ai

> Anthropic: Claude Sonnet 5: $2.00/M in, $10.00/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper.

[Leaderboard](/) / Anthropic

# Claude Sonnet 5

Anthropic · released 2026-06-30 · proprietary

 $4.00 per million tokens, balanced Balanced Summarise Chat Code gen Agentic

## Muse Spark 1.2 is both better and cheaper.

It scores **+1.5** higher and costs **50% less** ($2.00/M against $4.00/M) on this workload — and it does everything this model does.

[GLM 5.3](/models/z-ai__glm-5.3) is cheaper still (46% less) but drops no image, file input.

The same model via **batch** is 50% cheaper ($2.00/M) — same weights, different latency.

## Our take

 editorial — not a measurement

The volume workhorse and probably the best-value proprietary model in this dataset. $2/$10 with a full 1M-token window and 128k max output undercuts Sonnet 4.6 ($3/$15) by a third while raising the ceiling. The pricing history matters for planning: $2/$10 shipped as introductory pricing through 2026-08-31 with a scheduled rise to $3/$15 on 2026-09-01, and Anthropic has now confirmed that increase will not happen — so this is the standard rate, not a countdown. Use it as the default for anything that does not specifically need Opus-class reasoning.

 - **Unverified in this take:** Anthropic does publish a token-inflation figure, but it compares newer Claude models against EARLIER CLAUDE models — not against another vendor. The cross-vendor version of this claim is unsupported, so read any comparison to a non-Claude price with that in mind.

### Strengths

 - $2/$10 for a 1M context window and 128k output
- Introductory rate confirmed permanent — no September price rise
- Flat pricing across the full context window
- Adaptive thinking without the Opus price

### Weaknesses

 - Not competitive with Opus 5 on the hardest agentic tasks
- Uses the newer tokenizer, so ~30% token inflation vs Sonnet 4.6
- No independent LMArena or AA score captured for it in this pass

### Reach for it when

 - High-volume production traffic
- RAG and summarisation at scale
- Default choice when Opus is overkill

### Avoid it if

 - The workload is genuinely frontier-hard
- You need the absolute cheapest token price — open weights win there

Sources: platform.claude.com · platform.claude.com

### Every price dimension

| Input | $2.00 /M |
| --- | --- |
| Output | $10.00 /M |
| Cached input | $0.200 /M |
| Cache write | $2.50 /M |
| Cache write 1h | $4.00 /M |
| Web search | $0.01 /call |
| Batch discount | 50% |

### Independent scores

| Intelligence | 55.3 |
| --- | --- |
| Coding | 71.5 |
| Agentic | 49.7 |
| LMArena Elo | 1442.3 high |

Sources are listed separately rather than averaged. Across the 62 models both have scored they correlate at r = 0.806 — close agreement overall, but four models rank very differently between them, and a blended score would hide exactly those.

#### Best rankings by task

 - godotgamedev #4 of 43 1268
- htmlslides #4 of 34 1231
- agenticgamedev #5 of 34 1236
- webapps #6 of 56 1281
- fullstack #7 of 59 1283
- python-pptxslides #7 of 35 1249
- gamedev #9 of 144 1329
- androidnative #9 of 54 1249

### Capability

| Context window | 1M |
| --- | --- |
| Max output | 128K |
| Input modes | text, image, file |
| Tool use | yes |
| Reasoning | optional |
| Open weights | no |

### Provenance

| Price source | openrouter.ai |
| --- | --- |
| Fetched | 2026-08-24 |
| Quality data | verified |
| Cross-checked | vendor page |

Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max,...