Verified October 2026

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GPT-5.6 Terra vs Grok 4.3

A straight cost comparison of GPT-5.6 Terra (OpenAI) and Grok 4.3 (xAI Grok), per 1,000,000 tokens, verified October 2026.

$2GPT-5.6 Terra input / 1M
$1.25Grok 4.3 input / 1M
63%Blended price gap
Grok 4.3Cheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-5.6 TerraGrok 4.3Winner
Input / 1M$2$1.25Grok 4.3
Cached input / 1M$0.20$0.20Tie
Cache write / 1M$2.50—Grok 4.3
Output / 1M$12$2.50Grok 4.3
Context window1,050,0001,000,000GPT-5.6 Terra
Batch APIYesYesTie
Long-context tier> 272,000> 200,000—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
GPT-5.6 Terra$5+$3.13
Grok 4.3$1.88Cheaper

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadGPT-5.6 TerraGrok 4.3Winner
Classification
100K reqs · 500 in / 10 out
$112$65Grok 4.3
Data extraction
10K reqs · 2K in / 200 out
$64$30Grok 4.3
Support chat
1K reqs · 4K in / 600 out
$15.20$6.50Grok 4.3
Code review
2K reqs · 8K in / 1.5K out
$68$27.50Grok 4.3
Summarisation
1K reqs · 20K in / 1K out
$52$27.50Grok 4.3
Agent loop
5K reqs · 12K in / 2K out
$240$100Grok 4.3
Translation
500 reqs · 15K in / 15K out
$105$28.13Grok 4.3
Long-document QA
200 reqs · 120K in / 800 out
$49.92$30.40Grok 4.3
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$31.10$17.80Grok 4.3
Nightly batch
10K reqs · 10K in / 2K out
$220$87.50Grok 4.3

GPT-5.6 Terra wins 0 of 10, Grok 4.3 wins 10 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

Grok 4.3 is cheaper on both sides of the mix: $1.25 vs $2 per million input tokens, and $2.50 vs $12 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthGPT-5.6 TerraGrok 4.3Difference
100$1.52$0.65Grok 4.3 cheaper by $0.87
1,000$15.20$6.50Grok 4.3 cheaper by $8.70
10,000$152$65Grok 4.3 cheaper by $87
100,000$1,520$650Grok 4.3 cheaper by $870

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptGPT-5.6 TerraGrok 4.3Winner
0%$15.20$6.50Grok 4.3
50%$11.60$4.40Grok 4.3
80%$9.45$3.14Grok 4.3
95%$8.37$2.51Grok 4.3

Both models publish a cached-read rate, so the discount is comparable: GPT-5.6 Terra reads cache at $0.20 per million, Grok 4.3 at $0.20.

Which one should you pick?

GPT-5.6 Terra wins on none of the ten workloads above, while Grok 4.3 wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch.

Beyond price, the two differ on input rate ($2 vs $1.25); output rate ($12 vs $2.50); cache-write rate ($2.50 vs —); context window (1,050,000 vs 1,000,000).

Batch mode is available on GPT-5.6 Terra and Grok 4.3, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-5.6 Terra cheaper than Grok 4.3?

On a blended 1M-input-plus-250K-output workload, Grok 4.3 is cheaper by 63% ($1.88 vs $5). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-5.6 Terra or Grok 4.3?

GPT-5.6 Terra has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-5.6 Terra and Grok 4.3: rates rise once input passes it.

Do both models support prompt caching?

GPT-5.6 Terra bills cached reads at $0.20 per 1M; Grok 4.3 at $0.20.

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