# Claude Haiku 4.5 vs Claude Sonnet 5 (2026): side-by-side comparison

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/vs/claude-haiku-4-5-vs-claude-sonnet-5
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Claude Haiku 4.5 | Claude Sonnet 5 |
|---|---|---|
| Price | $5/1M out | $15/1M out ($10 intro until 2026-08-31) |
| Crowd score | 67% (2 votes) | 57% (3 votes) |
| provider | Anthropic | Anthropic |
| contextWindow | 200K tokens | 1M tokens (128K max output) |
| priceIn | $1/1M in | $3/1M in ($2 intro until 2026-08-31) |
| priceOut | $5/1M out | $15/1M out ($10 intro until 2026-08-31) |
| modalities | text, vision (input); text output | text, vision (image input, text output) |
| openWeights | no | no |
| reasoning (1-5) | 3 | 4.5 |
| coding (1-5) | 3.5 | 4.5 |
| writing (1-5) | 3 | 4.5 |
| speed (1-5) | 4.5 | 4 |
| valueForMoney (1-5) | 4 | 4.5 |

### Claude Haiku 4.5

> Anthropic's fastest model: about 90% of Sonnet 4.5's coding skill at $1/$5 per 1M tokens, 200K context.

Strengths:
- 73.3% on SWE-bench Verified, about 90% of Sonnet 4.5's agentic coding at one third of the price
- Fast: more than 2x Sonnet 4 speed per Anthropic, with launch customers reporting 4-5x faster than Sonnet 4.5; ~92-110 output tok/s measured by Artificial Analysis
- Devs report precise, localized code edits that avoid touching irrelevant code, better than GPT-5 mini class in early testing
- Supports both vision input and extended thinking, rare at this price tier at launch

Weaknesses:
- $5/1M output is pricey for a small model: Gemini Flash and GPT mini tiers undercut it several-fold on output-heavy tasks
- 200K context (vs 1M for Sonnet 5/Opus siblings) and 64K max output limit large-codebase and long-output work
- Mediocre cross-domain reasoning: users report weak results on GPQA, MedQA, MMMU style knowledge tasks

Verdict: Pick Haiku 4.5 if you are on the Anthropic stack and need near-Sonnet coding quality at low latency and a third of the price: it is a massive step up from Haiku 3.5 and excels as the worker model in multi-agent pipelines. It remains Anthropic's current small model as of July 2026, so it is the default cheap tier for Claude-based products. Avoid it for deep cross-domain reasoning, very large codebases (200K context cap), or pure cost-per-token shopping, where Gemini Flash and GPT mini tiers are now cheaper, and step up to Sonnet 5 when quality matters more than speed.

Full review: https://glad-ia-tor.com/tool/claude-haiku-4-5 · Markdown: https://glad-ia-tor.com/tool/claude-haiku-4-5.md

### Claude Sonnet 5

> Anthropic's most agentic Sonnet: near Opus 4.8 quality on coding and agents at $3/$15 with 1M context

Strengths:
- Large agentic gains over Sonnet 4.6: Terminal-Bench 2.1 80.4% vs 67.0%, OSWorld-Verified 81.2% vs 78.5%, SWE-bench Pro 63.2% vs 58.1%
- Matches Opus 4.8 on knowledge work (GDPval-AA v2: 1,618 vs 1,615) and nearly ties it on Humanity's Last Exam with tools (57.4% vs 57.9%) at 60% of Opus 4.8 pricing (40% during the intro window)
- 1M token context window and 128K max output; introductory pricing of $2/$10 per 1M tokens through Aug 31, 2026
- Persistent self-verifying agent behavior: hands-on reviews note it tests its own code and iterates on hard problems until solved, unlike Sonnet 4.6

Weaknesses:
- New tokenizer inflates token counts roughly 30% for the same text (1.0-1.35x per Anthropic; ~1.4x English, ~1.28x Python measured by Simon Willison), raising effective cost despite the unchanged sticker price
- Verbose and token-hungry: ~$2.29 per task vs ~$1.20 for Sonnet 4.6 in independent tests (ranked 101st of 161 for cost efficiency); at high effort cost-per-task can exceed Opus 4.8
- Measurably slower than Sonnet 4.6 on small routine edits and prone to over-engineering simple tasks (CodeRabbit hands-on review)

Verdict: Choose Sonnet 5 if you run coding, terminal or computer-use agents and want near Opus 4.8 quality at Sonnet prices, especially during the $2/$10 intro window; it is a strict upgrade over Sonnet 4.6 at low and medium effort. Budget for the new tokenizer and its verbosity: real per-task costs run well above Sonnet 4.6, and at the highest effort levels Opus 4.8 can be the better deal per solved task. Avoid it for latency-sensitive small edits or pipelines that rely on temperature and top_p, which now error. Sonnet 4.6 remains the pragmatic pick for high-volume tiny-diff workloads.

Full review: https://glad-ia-tor.com/tool/claude-sonnet-5 · Markdown: https://glad-ia-tor.com/tool/claude-sonnet-5.md

## More

Full llm-models ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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