Claude Opus 5 против GPT-5.6: цена, технические характеристики и реальные тесты

1. **Memory exhaustion from full-file load** — Replace `json.load()` with a streaming parser (`ijson.items(f, 'records.item')`) yielding one record at a time; transform lazily via generators, never materializing a list. *Validate:* run against the full file with RSS capped (`resource.setrlimit`) at ~500 MB; process must complete.

2. **Unbounded per-record overhead** — Batch records into fixed-size chunks (e.g. 500) before dispatch, keeping the per-call request body identical to today's contract. *Validate:* diff serialized payloads from old and new paths for a 1,000-record sample; require byte equality.

3. **Retry storms on transient failure** — Wrap API calls in exponential backoff with jitter and a max attempt cap; retry only on 5xx, 429, and connection/timeout errors, never 4xx. *Validate:* fault-injection stub returning 503 three times, then 200; assert attempt timestamps grow geometrically and a 400 aborts immediately.

4. **Duplicate writes after timeout** — Generate a deterministic idempotency key per record (hash of stable business fields) and send it in the existing header/field the API already accepts. *Validate:* force a timeout after server-side commit, replay, and assert one stored row.

5. **No recovery after crash** — Persist last committed offset/record ID to a checkpoint file, fsynced only after the API acknowledges the batch. *Validate:* SIGKILL mid-run, restart, assert zero gaps and zero reprocessed commits.

6. **Silent partial failures** — Route exhausted-retry records to a dead-letter file with error context; exit non-zero if non-empty. *Validate:* inject permanent 400s; confirm counts reconcile.

Picking an AI model is harder than it should be. Product pages tend to read the same, and benchmark scores rarely tell you how a model behaves during an ordinary workday.

GlobalGPT provides access to multiple AI models through one subscription, so you don't need a separate account with every provider. Claude Opus 5 is available on GlobalGPT, and so is GPT-5.6 Sol. Both handle coding, writing, analysis, and research well, though the better pick shifts depending on what you're doing.

**Test with your own prompts**

Users can compare the two models with the same prompt, so you can judge the actual answer, not the marketing copy. Results can vary by task. The model that writes elegant prose may not be the one that follows a strict JSON schema reliably, and the model that solves a coding problem quickly may spend more tokens on a long research synthesis.

Weigh output quality, cleanup effort, speed, and usage together. Because both models live in one place, side-by-side testing is fast — run a few prompts from your real work before picking a default.

## Calculations

| Week | Visitors | Purchases | Conv. rate | Refunds | Refund rate | Revenue | RPV |
|---|---|---|---|---|---|---|---|
| 1 | 1,000 | 120 | 120/1,000 = **12.0%** | 6 | 6/120 = **5.0%** | $3,240 | **$3.24** |
| 2 | 1,100 | 110 | 110/1,100 = **10.0%** | 5 | 5/110 = **4.5%** | $6,600 | **$6.00** (reported) |
| 2 adj. | 1,100 | 110 | 10.0% | 5 | 4.5% | $6,600 − $3,600 = $3,000 | **$2.73** |
| 3 | 950 | 130 | 130/950 = **13.7%** | 4 | 4/130 = **3.1%** | $3,510 | **$3.69** |

Supporting ratios (from given columns):

- Checkout→purchase: W1 120/180 = 66.7% · W2 110/210 = 52.4% · W3 130/190 = 68.4%
- Visitor→checkout: W1 18.0% · W2 19.1% · W3 21.1% (190/950 — highest of the three)
- Implied AOV: W1 $3,240/120 = $27.00 · W2 adj. $3,000/110 = $27.27 · W3 $3,510/130 = $27.00

## Most misleading source value

**Week 2 revenue of $6,600.** Taken at face value it yields RPV of $6.00 — roughly double every other week — and would suggest Week 2 was the best week. After removing the $3,600 enterprise invoice, Week 2 is the *worst* week on RPV ($2.73) and the worst on conversion (10.0%). The direction of the conclusion reverses entirely.

## Verified facts (from the table + source note)

- Week 3 has the highest conversion (13.7%), highest RPV ($3.69), and lowest refund rate (3.1%).
- Week 2 has the highest checkout starts in absolute terms (210) but the fewest purchases (110).
- Week 2's checkout→purchase rate (52.4%) is ~14 points below Weeks 1 and 3.
- $3,600 of Week 2 revenue is not attributable to these visitors.
- Adjusted AOV is stable at ~$27 across all three weeks.

## Assumptions (not established by the data)

- The $3,600 is the *only* contaminating entry; other weeks are assumed clean.
- Refunds are attributed to the week of purchase, not the week issued; if refunds lag, refund rates are understated for recent weeks.
- Visitors are counted consistently week to week and are comparable in traffic mix.
- Week 2's checkout drop reflects a funnel problem rather than a tracking or traffic-mix change.
- Three weeks is treated as a trend; with these counts, weekly swings could be noise.

## Recommended actions

**1. Audit the Week 2 checkout flow, keyed to the 52.4% checkout→purchase rate.** Applying the ~67% completion seen in Weeks 1 and 3 to Week 2's 210 checkout starts implies ~141 purchases; the actual 110 is a shortfall of ~31 orders, or ~31 × $27 ≈ **$840 in lost revenue that week**. Pull payment errors, promo-code failures, and shipping/tax display for that date range.

**2. Re-baseline reporting on adjusted RPV and replicate Week 3, keyed to RPV $3.69 vs. $2.73.** Restate Week 2 to $3,000 and flag non-visitor revenue in a separate line going forward. Then identify what changed in Week 3 — its visitor→checkout rate of 21.1% and 13.7% conversion produced $0.96 more per visitor than adjusted Week 2, worth ~$1,050 if applied to Week 2's 1,100 visitors.

**Findings**

1. **Desktop test scale and baseline (Source A, [A1]):** Over three weeks, 12,000 desktop visits on the original page produced 1,920 checkout starts and 1,056 completed purchases — a 55% completion rate among those who started checkout.

2. **Variant effect on completion (Source A, [A2]):** The variant, which moved pricing disclosure above the payment form, produced 2,040 starts and 1,224 purchases from 12,100 visits — a 60% start-to-purchase completion rate, versus 55% for the original.

3. **Isolated change, limited population (Source A, [A2], [A3]):** Source A tested a single change (pricing disclosure placement), so its result is attributable to that change alone; however, it excluded mobile traffic entirely.

4. **Bundled design limits attribution (Source B, [B1], [B2]):** The later two-week test combined three changes — earlier pricing disclosure, shorter form copy, and a trust badge — and reported a completion increase from 52% to 58%, but the report did not separate the three effects.

5. **Different traffic mix (Source B, [B1]):** The second test covered 8,400 visits, 72% of them mobile, so it measured a substantially different population than the desktop-only test in Source A.

6. **Support-ticket signal and power (Source B, [B3]):** Unexpected-price tickets were identical at 31 in each arm, and the analyst warned the test was underpowered to detect support-ticket differences.

**Material conflict (≤60 words)**

Source A reports unexpected-price tickets falling 84→39 with earlier pricing disclosure ([A3]); Source B reports no change, 31 versus 31, with a variant that included the same disclosure ([B3]). This matters because the ticket reduction is the main support-cost justification, and it may not hold on mobile — though Source B admits it was underpowered.

**Verification checklist**

1. Confirm the completion rates: recompute 1,056/1,920 and 1,224/2,040 from [A1]–[A2], and confirm whether Source B's 52%/58% ([B2]) uses the same denominator (checkout starts) or total visits.
2. Obtain the per-change breakdown for Source B's bundled variant ([B1]–[B2]) to determine how much of the 6-point gain came from pricing disclosure.
3. Request the power calculation and ticket-volume baselines behind [B3] to establish whether 31 versus 31 could conceal an effect of the size seen in [A3].