Claude Haiku 5.5 vs GPT-6 Luna vs Gemini 3.8 Flash Comparison
We benchmarked Claude Haiku 5.5, GPT-6 Luna, and Gemini 3.8 Flash across 13 tasks. Here is how their pricing, speed, tokenizers, and real-world results stack up.
Update (9 Nov 2026): Claude Fable 5.2 and Mythos 5.2 are out - prices, access and benchmarks are in my Claude Fable 5.2 guide.
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Claude Haiku 5.5, GPT-6 Luna, and Gemini 3.8 Flash are currently three of the most compelling budget AI models on the market. I ran all three through the same 13 standardized tasks in my testing lab (we tested them in Polish): Luna came out on top with 82.8 points, ahead of Gemini 3.8 Flash at 79.4 and Haiku 5.5 at 76.8. However, a benchmark score never tells the whole story - non-English tokenization overhead, raw latency, and specific agent capabilities also dictate which model actually fits your pipeline. You can read the standalone breakdown in my guide to Claude Haiku 5.5, but here I pit it directly against its rivals to show you which one to pick.
Status as of October 8, 2026
- Best overall in Polish language tasks: GPT-6 Luna - 82.8 out of 100 points.
- Cheapest per 1,000 Polish queries: GPT-6 Luna - approx. $0.50; Haiku 5.5 - approx. $0.81; Gemini 3.8 Flash - approx. $3.39.
- Best for agent workflows (computer use, sub-agents): Claude Haiku 5.5.
- Highest precision in rigorous proofreading and factual queries: Gemini 3.8 Flash (30/30 and 48/48).
- Haiku 5.5 token penalty: identical Polish text consumes 34% more tokens than on Haiku 4.5.
Lab Results: 13 Standardized Benchmark Tasks
I follow a strict test protocol: 13 distinct prompts (an article, product description, email, LinkedIn post, short story, headline generation, summary, translation, two proofreading challenges, and two factual trivia tests), fixed token boundaries, and API execution via OpenRouter. Outputs are evaluated by third-party judge models (GPT-6 Sol and Gemini 3.1 Pro), with scores calibrated against permanent anchor models so rounds from different dates can be directly compared. We tested it in Polish; Haiku 5.5 was run on October 8, 2026, the day after its public launch.
| Model | Score (out of 100) | Difficult Proofreading | Difficult Fact Quiz | Cost for 13 Tasks |
|---|---|---|---|---|
| GPT-6 Luna | 82.8 | 29/30 | 48/48 | $0.004 |
| Gemini 3.8 Flash | 79.4 | 30/30 | 48/48 | $0.036 |
| Claude Haiku 5.5 | 76.8 | 26/30 | 43/48 | $0.011 |
| DeepSeek V4.1 Flash | 72.9 | 30/30 | 47/48 | $0.023 |
| Claude Haiku 4.5 (baseline) | 59.2 | 23/30 | 40/48 | $0.069 |
Key takeaways from the run:
- Haiku 5.5 achieved the biggest generational leap in its product family: +17.6 points compared to Haiku 4.5. The email draft, LinkedIn post, and creative narrative scored 8.2-8.3 out of 10 from the judges.
- Its weakest result was a 5-bullet summary (5.8 points): Haiku exceeded the 80-word boundary and produced a syntax flaw flagged by both judges.
- Luna took first place overall, yet recorded the lowest single task score in the group: its product description received just 5.3 points.
- Gemini 3.8 Flash and DeepSeek V4.1 Flash made zero mistakes in the difficult proofreading task. Haiku 5.5 missed 4 errors out of 30.
Track the complete comparative table in the Testing Lab, or view side-by-side prompt responses in the Model Arena.
Pricing: List Rates vs Real API Bills
On paper, Haiku 5.5 and Luna cost exactly the same: $0.10 per million input tokens and $0.50 per million output tokens. Gemini 3.8 Flash looks 7.5 times more expensive on the rate card. In production, however, tokenizer efficiency changes everything depending on how each architecture splits non-English text into subwords.
| Model | API Price (1M Tokens In / Out) | Tokens per Identical Non-English Text* | Est. Cost for 1,000 Queries | Context Window |
|---|---|---|---|---|
| GPT-6 Luna | $0.10 / $0.50 | 210 | approx. $0.50 | 1.05M |
| Claude Haiku 5.5 | $0.10 / $0.50 | 341 | approx. $0.81 | 1M |
| DeepSeek V4.1 Flash | $0.30 / $1.20 | 225 | approx. $1.31 | 1M |
| Gemini 3.8 Flash | $0.75 / $3.75 | 192 | approx. $3.39 | 1M |
| Claude Haiku 4.5 | $1.00 / $5.00 | 254 | approx. $5.98 | 200K |
* An identical snippet of Polish text that takes 144 tokens in GPT English. The 1,000-query cost is calculated in our AI Price Tracker based on a baseline workload of 400 input tokens and 600 output tokens (English baseline equivalent), multiplied by each model’s tokenizer expansion ratio. Prices exclude sales tax/VAT, recorded as of October 8, 2026.
Three nuances you must account for:
- Haiku 5.5 token expansion. The sample text required 341 tokens where Haiku 4.5 used 254 - a 34% increase. Anthropic’s documentation notes an average inflation of roughly 30% across natural language. Because of this, despite identical token list prices, Haiku ends up roughly 60% more expensive than Luna on non-English payloads.
- Reasoning tokens are billed as output. At maximum reasoning effort, Artificial Analysis reports Haiku 5.5 generates roughly 162,000 output tokens per complex query, three times more than Luna. In our run with effort set to “low”, reasoning consumed 12,400 tokens across 13 tasks.
- Gemini 3.8 Flash rates are promotional through December 31, 2026, after which Google may adjust pricing.
Haiku holds one distinct cost advantage: prompt caching read hits cost just $0.01 per million tokens. If you pass an identical massive system prompt thousands of times, cache savings can easily offset the tokenizer expansion penalty.
Inference Latency
We measured the wall-clock execution time of 9 core text tasks from the test suite via OpenRouter, capturing real-world network overhead and queuing delays.
| Model | Time for 9 Tasks |
|---|---|
| GPT-6 Luna | 39 s |
| Gemini 3.8 Flash | 69 s |
| Claude Haiku 5.5 | 73 s |
| DeepSeek V4.1 Flash | 365 s |
Luna is nearly twice as fast as Haiku 5.5 because it keeps thinking phases short and produces more concise phrasing. Haiku 5.5 generated an average of 164 tokens per second (compared to 101 on Haiku 4.5 and 98 on Sonnet 5.5), though raw tokens per second cannot be directly compared across providers due to disparate tokenizers. DeepSeek V4.1 Flash suffered significant routing delays on OpenRouter, making it unsuited for real-time conversational UIs.
Vendor Benchmarks vs Independent Evaluations
While my testing focuses on non-English linguistic nuance, agentic workloads require looking at international benchmarks:
- OSWorld 2.1 (computer interaction, Anthropic data): Haiku 5.5 reaches 72.4%, Luna hits 48.9%.
- Terminal-Bench 4.0 (CLI execution, Anthropic data): Haiku 5.5 scores 39.2%, Luna scores 16.4%.
- Artificial Analysis Quality Index: Haiku 5.5 scores 43 (at max reasoning), Gemini 3.8 Flash scores 41, Luna scores 38.
- Hallucinations on AA-Omniscience: Haiku 5.5 logs 40%, Luna logs 77% - Haiku is far more willing to admit when information is missing.
These figures come from Anthropic and Artificial Analysis rather than my own lab runs, but the trend is clear: Luna provides superior text generation for non-English prose, while Haiku dominates structured agent environments.
Which Budget AI Model Should You Choose? 5 Scenarios
- High-volume content generation (marketing, emails, newsletters) - GPT-6 Luna. Luna delivers the highest general score, the lowest bill per 1,000 queries, and the fastest completion times. Its main weakness is e-commerce product copy, where you should supply explicit style few-shot examples. Read our deep dive on GPT-6 Luna.
- Browser agents, Claude Code sub-agents, tool-use automation - Claude Haiku 5.5. Outstanding results on OSWorld and Terminal-Bench, a full 1-million-token context window, and significantly fewer hallucinations make Haiku 5.5 the premier operational model. Check out the setup tutorial in Haiku 5.5 in Claude Code.
- Strict proofreading, factual validation, and high-liability tasks - Gemini 3.8 Flash. A perfect 30/30 on difficult proofreading and 48/48 on factual recall. It carries a higher list price than Haiku and Luna, but remains roughly five times cheaper than Sonnet 5.5. Learn more in our Gemini guide.
- Existing Anthropic API integrations: classification, parsing, routing - Claude Haiku 5.5. Upgrading from Haiku 4.5 requires swapping a single model identifier string while cutting your API bill by 75-90%. No contract changes, no architectural refactoring.
- Self-hosted infrastructure, open weights, non-urgent batch tasks - DeepSeek V4.1 Flash. Flawless proofreading and 47/48 on factual quizzes paired with the freedom of open weights. Hosted API latency is currently sluggish, so reserve it for asynchronous queues. See our analysis of DeepSeek.
Free Chat Access
- Claude Haiku 5.5 - available directly within the free Claude tier (web, iOS, Android) via the model picker.
- Gemini 3.8 Flash - accessible in the Gemini consumer app.
- GPT-6 Luna - offered via API, ChatGPT Work, and Codex. Free consumer chat availability has not been confirmed.
- DeepSeek V4.1 Flash - accessible via official API endpoints and OpenRouter.
The Verdict
For high-volume non-English text pipelines, GPT-6 Luna remains my primary recommendation: it generates better prose at a lower total cost. Claude Haiku 5.5 is my preferred choice for agent frameworks and native Anthropic workflows: dispatching sub-agents, structuring messy data, and analyzing massive documents across its 1-million-token context window. Keep Gemini 3.8 Flash on hand whenever zero-tolerance proofreading and strict factual verification are required. Before committing to large-scale production, benchmark 50 to 100 sample prompts against your specific domain - tokenizer density differences can easily overturn the nominal rate cards.
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- In-house lab evaluations: 13 benchmark tasks, judge models GPT-6 Sol and Gemini 3.1 Pro, Haiku 5.5 evaluation run completed on October 8, 2026.
- Pricing and tokenizer ratios: AI Price Tracker, updated October 8, 2026.
- Anthropic, Introducing Claude Haiku 5.5 - official pricing and vendor benchmark declarations.
- Anthropic, Claude Haiku 5.5 Documentation - tokenizer details, context windows, and reasoning effort controls.
- Artificial Analysis, Claude Haiku 5.5 Report - Quality Index scores, token consumption curves, and hallucination rates.
Verified on October 8, 2026.
