OpenAI is talking about coding. We only care about translation.
OpenAI released GPT-5.6 on July 9, 2026. The naming changed: the number is the generation, and Sol / Terra / Luna are capability tiers that can advance independently. Sol has the highest reasoning ceiling, Terra is the balanced default, Luna is the lightweight speed-and-cost option.
The launch material is about agentic coding and token efficiency — nothing to do with document translation. So instead of echoing "GPT-5.6 translates better," we went after the question OpenAI won't answer and no competing review has data for:
The tiers are priced 5× apart (Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per 1M input/output tokens). For translating documents, which tier should you actually pay for?
The short answer, up front:
- For clean documents — papers, contracts, technical manuals, tables — pick Sol. It scores a perfect 5.0 on five of our eight scenarios and clearly beats the identically-priced GPT-5.5.
- Terra is the hardest tier to justify. It beats Luna by 0.04 points while costing 42% more per point of quality.
- Do not use GPT-5.6 on scanned documents. All three tiers fall behind the previous-generation GPT-5.5 at recovering from OCR errors. Terra copies misrecognized English words straight into the Chinese translation.
How we tested
Eight document scenarios, each probing one specific weakness:
| Scenario | Direction | What it probes |
|---|---|---|
| Academic abstract | EN → ZH | Jargon, passive voice, formal register |
| Legal contract clause | EN → ZH | Nested long sentences, precision, legalese |
| Technical doc with code | EN → ZH | Identifiers inside backticks must stay untranslated |
| Literary prose (Lu Xun) | ZH → EN | Classical-flavored register, rhythm, imagery |
| Manga dialogue | JA → EN | Colloquial register, character voice, sentence-end particles |
| Markdown table | EN → ZH | Table structure, error codes, units |
| Long-document term consistency | EN → ZH | One polysemous term across paragraphs, translated consistently |
| OCR noise | EN → ZH | Source contains scan artifacts; the model should recover intent, not copy errors |
The first five reuse the exact source texts from our April DeepSeek V4 review, unchanged, so the two evaluations can be compared over time. The last three are new, and target what BelinDoc users actually run into.
Seven models: GPT-5.6 Sol / Terra / Luna, the previous-generation GPT-5.5, plus Gemini 3.1 Pro, Claude Opus 4.8, and DeepSeek V4 Pro.
Every cell ran three times; we take the median. This matters more than it sounds. A single call is subject to server-side variance, and drawing conclusions from one sample is how luck gets published as a finding.
Dual blind judging, and the judges are not contestants. Candidate translations are anonymized to A/B/C… (reshuffled independently each round) and scored 1–5 by GPT-5.4 and Claude Opus 4.7 on faithfulness, fluency, terminology, style, and formatting. Neither judge appears in the contestant list — letting a model grade itself introduces bias, which was a flaw in our previous review and is fixed here.
All 168 raw translations, every judge score, and the aggregation scripts live under docs/evaluations/gpt-5-6/ in our repo.
Overall ranking
| Rank | Model | Avg | Faithful | Fluency | Term | Style | Format | Cost/call | Cost/point |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Claude Opus 4.8 | 4.73 | 4.8 | 4.7 | 4.8 | 4.4 | 5.0 | $0.00366 | $0.00077 |
| 2 | Gemini 3.1 Pro | 4.71 | 4.8 | 4.7 | 4.8 | 4.6 | 4.8 | $0.01222 | $0.00259 |
| 3 | GPT-5.6 Sol | 4.70 | 4.8 | 4.8 | 4.8 | 4.7 | 4.5 | $0.00360 | $0.00077 |
| 4 | GPT-5.5 | 4.54 | 4.7 | 4.4 | 4.6 | 4.3 | 4.7 | $0.00393 | $0.00087 |
| 5 | GPT-5.6 Terra | 4.38 | 4.4 | 4.3 | 4.4 | 4.3 | 4.6 | $0.00161 | $0.00037 |
| 6 | GPT-5.6 Luna | 4.34 | 4.4 | 4.3 | 4.3 | 4.1 | 4.6 | $0.00113 | $0.00026 |
| 7 | DeepSeek V4 Pro | 4.18 | 4.1 | 4.0 | 4.1 | 4.1 | 4.5 | $0.00068 | $0.00016 |
The top three are separated by 0.03 points, which is noise. Do not read this as "Claude beat Sol." The informative gaps are further down the table.
Cost/point is cost per call divided by the average score: what you pay for a unit of quality.
Picking a tier
Sol: the right answer for clean documents
Sol earns a perfect 5.0 on academic abstracts, legal clauses, technical docs, Markdown tables, and term consistency — five of eight scenarios. It doesn't drop points on the things that stress long context, like nested legal sentences and keeping one term consistent across paragraphs.
Its weak spot is formatting (4.5), second-lowest in the field. The next section explains why.
Terra: the tier that's hard to explain
| Avg score | Cost/call | Cost/point | Median latency | |
|---|---|---|---|---|
| Sol | 4.70 | $0.00360 | $0.00077 | 4,640 ms |
| Terra | 4.38 | $0.00161 | $0.00037 | 3,598 ms |
| Luna | 4.34 | $0.00113 | $0.00026 | 7,998 ms |
Terra costs half what Sol does and gives up 0.32 points. It beats Luna by 0.04 points and costs 42% more per point.
For document translation, that leaves it stranded: go Sol for quality, go Luna to save money. Terra's one real advantage is that it is the fastest model we tested (3,598 ms). If latency dominates your requirements, it earns its place.
Luna: cheapest, but not fastest
This was the most counterintuitive result. OpenAI positions Luna as the fast, cheap tier. It is indeed the cheapest — but it was the slowest of the three in our tests, with a median latency of 7,998 ms, 2.2× Terra's.
One caveat: every call went through the same third-party relay, so the latency spread may reflect routing rather than the model. But on that path, "Luna means fast" does not hold.
The main finding: GPT-5.6 handles scanned documents worse than GPT-5.5
This is the result worth pulling out on its own, and the one BelinDoc users most need to know.
We built an English source containing typical OCR artifacts — module misread as rnodule (r+n fused into m), 0.5 as O.5 (zero read as the letter O), inspection as inspecti0n, plus a hyphenated line break:
The rnodule shall be calibrated at 25 °C be-
fore shipment. Any devi ation greater than O.5 % must
be recorded in the inspecti0n report and signed by the
quality engineer.
A competent translator model should see through this to "module," "0.5%," and "inspection report," and translate the intent — not copy the typos.
Scores (out of 5):
| Model | Avg | Faithful | Term | Format |
|---|---|---|---|---|
| Claude Opus 4.8 | 5.0 | 5.0 | 5.0 | 5.0 |
| Gemini 3.1 Pro | 4.4 | 4.5 | 4.0 | 3.5 |
| GPT-5.5 (previous gen) | 4.3 | 4.0 | 4.0 | 3.5 |
| GPT-5.6 Sol | 3.4 | 4.0 | 4.0 | 2.0 |
| GPT-5.6 Luna | 3.0 | 3.0 | 1.5 | 3.5 |
| GPT-5.6 Terra | 2.2 | 2.5 | 1.5 | 2.0 |
| DeepSeek V4 Pro | 1.7 | 2.0 | 1.0 | 2.0 |
The translations make it obvious.
Claude Opus 4.8 — every artifact corrected:
模块应在出厂前于25 °C下进行校准。任何大于0.5 %的偏差必须记录在检验报告中，并由质量工程师签字。
GPT-5.6 Sol — recovers "module" and "inspection report," but leaves O.5 % untouched and carries the English hyphenated line break into Chinese:
该模块应在 25 °C 下进行校准，然- 后再发货。任何大于 O.5 % 的偏差都必须 记录在检验报告中，并由 质量工程师签字。
GPT-5.6 Terra — leaves the misrecognized English words sitting inside the Chinese translation:
该 rnodule 应在 25 °C 下进行校准，然 后再发货。任何大于 O.5 % 的偏差都必须记录在 inspecti0n report 中，并由 质量工程师签字。
GPT-5.6 Luna — same failure:
该 rnodule 应在发货前于 25 °C 下进行校准。任何大于 O.5 % 的偏差都必须记录在 inspecti0n report 中，并由质量工程师签字。
Sol's 然-\n后 is also why its formatting score is only 4.5: it treated layout noise in the source as formatting worth preserving.
So if you translate scans, photos, or any OCR'd PDF, none of the GPT-5.6 tiers is a good choice. Use Claude Opus 4.8. If you must stay inside OpenAI, the previous-generation GPT-5.5 is the safer pick.
Same price, new generation: is there any reason left to run GPT-5.5?
GPT-5.5 and GPT-5.6 Sol carry identical list prices: $5 per 1M input tokens, $30 per 1M output.
Sol scores 4.70; GPT-5.5 scores 4.54. Scenario by scenario, Sol is clearly ahead on legal (5.0 vs 4.3), technical docs (5.0 vs 4.6), tables (5.0 vs 4.6), and term consistency (5.0 vs 4.5).
Same money, better quality. Except on scans.
Which is exactly why the previous section stands on its own: the one job GPT-5.5 is still better at is the one where its successor regressed (4.3 vs 3.4).
Across vendors: where the money actually goes
Sort by cost per point and the picture changes completely:
| Model | Avg score | Cost/point | vs Sol |
|---|---|---|---|
| DeepSeek V4 Pro | 4.18 | $0.00016 | 79% cheaper |
| GPT-5.6 Luna | 4.34 | $0.00026 | 66% cheaper |
| GPT-5.6 Terra | 4.38 | $0.00037 | 52% cheaper |
| GPT-5.6 Sol | 4.70 | $0.00077 | — |
| Claude Opus 4.8 | 4.73 | $0.00077 | same |
| GPT-5.5 | 4.54 | $0.00087 | 13% more |
| Gemini 3.1 Pro | 4.71 | $0.00259 | 236% more |
Two things stand out.
Claude Opus 4.8 and Sol cost the same per point of quality — but Claude scores higher overall, takes formatting 5.0, and takes OCR 5.0. For the same money you get a more complete model.
Gemini 3.1 Pro is the most expensive thing here. Its list price ($2/$12) undercuts Sol, but it emits 995 output tokens per call on average, 917 of which are reasoning tokens — and reasoning tokens bill at the output rate. It legitimately ranks second on quality, but costs 3.4× Sol per point.
DeepSeek V4 Pro is the cheapest by far, at a fifth of Sol's cost per point. The price is last place overall (4.18) and a collapse on OCR (1.7, the worst in the field). For high-volume, low-stakes, clean text it is unbeatable value. Never point it at a scan.
An aside: stability under concurrency
We fired all 168 translation calls at a concurrency of 12: zero failures, zero retries, finished in 159 seconds.
This is worth stating because "model X's API is flaky" gets repeated constantly, usually on the strength of one or two unlucky calls. Three repeats per cell and 168 calls total is what it took before we were willing to say this path is stable at this concurrency.
A single failure doesn't prove a model is unstable, any more than a single success proves it isn't.
Verdict and limits
How to choose:
- Papers, contracts, technical manuals, documents with tables → GPT-5.6 Sol
- Scans, photos, any OCR'd PDF → Claude Opus 4.8 (second choice: GPT-5.5)
- High volume, cost-sensitive, clean source text → GPT-5.6 Luna or DeepSeek V4 Pro
- Latency-critical → GPT-5.6 Terra (fastest, and that's its whole case)
- Already on GPT-5.5 → upgrade to Sol, same price, better output — except on scans
What this review does not establish:
- The corpus is eight short passages, not full-length documents. A real 100-page PDF adds attention decay, tables spanning pages, and figure captions interleaved with body text. Short-sample results don't map 1:1.
- All calls went through one third-party relay. Latency reflects that path, not a direct-to-vendor connection.
- The judges are LLMs, not human translators. LLM judges are reasonably reliable on checkable dimensions like faithfulness and terminology; treat "style" with more caution.
- This is one evaluation, not a running benchmark. Models change; conclusions expire.
❓ FAQ
What is the difference between GPT-5.6 Sol, Terra, and Luna?
They are three capability tiers of the same generation, not three versions. Sol has the highest reasoning ceiling at $5 input / $30 output per 1M tokens; Terra is the balanced tier at $2.50/$15; Luna is the lightweight tier at $1/$6. Across our eight document-translation scenarios they scored 4.70, 4.38, and 4.34 out of 5 respectively.
Which GPT-5.6 tier should I use for document translation?
Use Sol for clean documents — papers, contracts, technical manuals, tables — where it scores a perfect 5.0 on five of eight scenarios. Use Luna for high-volume, cost-sensitive work: it trails Terra by only 0.04 points while costing 42% less per point of quality. Terra's only edge is speed (3,598 ms median latency). For scanned documents, none of the three is a good choice.
Is GPT-5.6 better than GPT-5.5 for translation?
Usually yes. Both carry the identical list price of $5/$30 per 1M tokens, and Sol scores 4.70 against GPT-5.5's 4.54, winning clearly on legal clauses, technical documents, tables, and term consistency. The one exception is OCR error recovery on scanned text, where GPT-5.5 is actually stronger (4.3 vs 3.4).
Can GPT-5.6 translate scanned PDFs?
It can, but it does so poorly. On source text containing OCR artifacts, the three tiers scored Sol 3.4, Luna 3.0, and Terra 2.2 — all below the previous-generation GPT-5.5 at 4.3. Terra and Luna copy misrecognized English words such as rnodule and inspecti0n straight into the translated output. For scans, use Claude Opus 4.8, which scored a perfect 5.0 on this scenario.
Is GPT-5.6 better than Claude Opus 4.8 or Gemini 3.1 Pro at translation?
Overall scores are Claude Opus 4.8 at 4.73, Gemini 3.1 Pro at 4.71, and GPT-5.6 Sol at 4.70 — a spread small enough to be noise, so no model wins on quality alone. But Claude matches Sol exactly on cost per point ($0.00077) while taking a perfect 5.0 on both formatting and OCR, and Gemini 3.1 Pro costs 3.4× Sol per point because of heavy reasoning-token usage.
How much does it cost to translate a document with GPT-5.6?
At official API pricing, one short-document translation call measured in this review cost roughly $0.00360 with Sol, $0.00161 with Terra, and $0.00113 with Luna. For comparison, Claude Opus 4.8 cost $0.00366 and DeepSeek V4 Pro just $0.00068. Real-world cost scales with document length and output token count.
Is GPT-5.6 Luna really the fastest tier?
Not in our testing. Luna's median latency was 7,998 ms — the slowest of the three tiers and about 2.2× Terra's 3,598 ms. Note that all calls went through the same third-party relay, so the gap may reflect routing rather than the model itself.
Test it on your own documents
This review used eight short passages. Your documents are longer and stranger — contracts with numbered clauses, papers with equations and figures, scans of wildly varying quality.
The best way to decide: upload your own document and compare.
👉 Upload your PDF / EPUB / Word and start translating
BelinDoc lets you switch translation models on the fly, keeps your original layout intact, and lets you compare multiple models on the same file from a single upload.
🔗 Related reading
- [Review] 📊 DeepSeek V4 Document Translation Review: vs V3.2, GPT-5.4, Claude 4.7 & Gemini 3 Pro
- [Review] 📊 GPT-5.2 Document Translation Review
- [Review] 📊 Gemini 3 Pro Translation Performance Review
- [Guide] 🎯 AI Model Selection Guide by Document Type
- [Scans] 🔍 Translating Scanned PDFs: An OCR Guide to Fixing Recognition Errors
