Claude Opus 5 vs GPT 6 Astra Stable: 4× the price for 5% more context

Read the effective rate, not the multiplier

The multipliers invite a wrong conclusion. Astra is 0.2× and Opus 5 is 0.1×, which looks like a 2× gap. But a multiplier is applied to that model’s own official rate, and the official rates are not the same: Opus 5 is $5 per million input and $25 output, while Astra is $10 and $50.

Do the arithmetic and the real gap is 4×. Opus 5 bills at $0.50 per million input and $2.50 output. Astra bills at $2.00 and $10.00. The multiplier halves Astra’s advantage-looking number, and the doubled base rate hands it straight back.

This is the general trap on any cross-model price comparison here: two multipliers are only comparable when the underlying rates match. Multiply first, then compare.

What the extra 50,000 tokens of context are for

Astra takes 1,050,000 tokens of context; Opus 5 takes 1,000,000. That is 5% more room. Both cap output at 128,000 tokens per response and both list text, tools and thinking, so context is the only specification separating them at all.

Five percent is not a category difference. If a prompt overflows 1,000,000 tokens, it is usually not 1,020,000 tokens long — it is a repository dump or a transcript that will keep growing, and 1,050,000 buys you one more turn before the same wall. Where the margin does matter is a workload that has been measured and sits just over the line.

Everything else about the window is identical in the way that matters to a bill: both charge per token regardless of how full it is, so a near-full request on Astra costs 4× what the same request costs on Opus 5.

One is a pool lane, the other is not

These two ids are not the same kind of thing. claude-opus-5 is served from the shared account pool. stable-gpt-6-astra is a stable-* id: a dedicated lane served on our own key, which does not participate in pool failover. That is a routing property, not a quality one — the weights are the same weights either way.

It also explains part of the price. Every stable-* lane costs more than its pool equivalent, and Astra’s 0.2× is the cheapest multiplier among them — the Claude stable lanes are all 0.4×.

One detail surprises people: Astra is an OpenAI-lineup model, but it answers on /v1/messages, the Anthropic-shaped route, not /v1/responses like the other GPT ids. So on request shape, Astra and Opus 5 actually are interchangeable — same endpoint, same headers, same body. The other GPT ids are not.

for MODEL in claude-opus-5 stable-gpt-6-astra; do
  echo "=== $MODEL ==="
  curl -sS https://token-share.app/v1/messages \
    -H "x-api-key: $TOKEN_SHARE_KEY" \
    -H "anthropic-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d "{
      \"model\": \"$MODEL\",
      \"max_tokens\": 2048,
      \"messages\": [{\"role\": \"user\", \"content\": \"Plan a migration for this schema change.\"}]
    }"
done

The comparison that is actually apples to apples

If the lane is what you want, compare Astra against the other dedicated lane at the top of the Claude lineup. stable-claude-opus-5 is 0.4× on $5/$25, which is $2.00 per million input and $10.00 output — exactly Astra’s effective rate, to the cent.

At that point the price column is a tie and the remaining differences are context (1,050,000 against 1,000,000) and which lineup’s weights you want. Both are dedicated lanes, both answer on /v1/messages, both cap at 128,000 output tokens.

And if the lane is not what you want, the comparison collapses back to $0.50/$2.50 against $2.00/$10.00, where you are paying 4× for 5% more context plus a different vendor’s model. Whether that is worth it is a question about which weights suit your work — run both on the same tasks and read the outputs, because no column in the catalog answers it.

Common questions