Kimi K3.1 Pro vs GPT-6 Astra: A Fifth of the Price, and Two Points the Other Way

GPT-6 Astra costs five times what Kimi K3.1 Pro costs on both billing lines: $10.00 per million input tokens against $2.00, and $50.00 per million output against $10.00. On the independent index both models are measured on, the cheaper one is ahead — Artificial Analysis scores Kimi K3.1 Pro at 55 and GPT-6 Astra at 53. On the cheaper model's own vendor table, though, the gap is not uniform: Kimi wins the terminal-agent benchmark by 7.5 points and loses the CAD and hallucination benchmarks outright. The honest version of this comparison is that you are not choosing between a better and a worse model. You are choosing how much a point of accuracy is worth to you, and whether you need to own the weights.

Both models are current flagships with a million tokens of context, both are gated or restricted in some way, and both are callable through OrcaRouter at their published list prices. The differences that decide a migration are narrower than the price multiple suggests, and three of them are measurable.

The two rate cards and what they actually bill

• Input — $2.00 per million tokens on Kimi K3.1 Pro against $10.00 on GPT-6 Astra.

• Output — $10.00 against $50.00. Output is where agentic work spends most of its money, which makes this the line that matters most.

• Cache — both vendors apply a 90% cache discount; Artificial Analysis records the same figure for both. A cache hit is therefore five times cheaper on Kimi in absolute terms, because it is 10% of a fifth of the price.

• Context — 1,048,576 tokens on Kimi K3.1 Pro, 1,050,000 on GPT-6 Astra. Effectively identical.

• Maximum output — 943,718 tokens on Kimi K3.1 Pro against 128,000 on GPT-6 Astra. This is the least discussed and most consequential difference on the card. A model that can write 900,000 tokens in one response can finish a large migration, generate a long document, or carry an agent through an unbounded loop without a client-side stitching strategy. Astra's 128K ceiling is generous by 2025 standards and restrictive next to this one.

The per-token prices understate the gap and overstate it at the same time, which is why the evaluator's cost-per-task figure is the useful one. Artificial Analysis puts Kimi K3.1 Pro at $2.11 per Intelligence Index task and GPT-6 Astra at $3.26 — a 35% saving, not an 80% one. The difference is verbosity: Kimi K3.1 Pro generated 150 million output tokens during the evaluation against Astra's 60 million. A cheaper token that is emitted two and a half times as often is only somewhat cheaper.

On a mixed request the arithmetic is blunt. A call that reads a million input tokens and writes 200,000 output tokens costs $2.00 plus $2.00 — $4.00 — on Kimi K3.1 Pro, and $10.00 plus $10.00 — $20.00 — on GPT-6 Astra. An agent that re-reads a cached repository with a 90% hit rate narrows the input side to almost nothing on both, and the output line alone is still $10 against $50 for the same 200,000 tokens.

Freshness is a second, less obvious cost. Artificial Analysis records GPT-6 Astra's knowledge cutoff at 30 April 2026, roughly six months before this comparison. Kimi K3.1 Pro is a post-training release on a model whose pre-training date Moonshot has not published, so neither side gives you a training cutoff you can put in a compliance document — but only one of the two has a cutoff the evaluator could verify at all.

The independent index, and the vendor's own table, disagree about the order

Artificial Analysis measures Kimi K3.1 Pro at 55 (4th of 215 models) and GPT-6 Astra at 53 (8th of 215), on version 4.3.2 of its index. That is a two-point lead for the model that costs a fifth as much. It is also within the range where harness and effort settings can flip the order, so treat it as "these are peers" rather than "one is better".

Moonshot's launch table, run on Moonshot's harness, puts the two models much further apart on the benchmark it headlines — and closer, or inverted, everywhere else:

• Terminal-Bench 4.0 — 65.7% for Kimi K3.1 Pro against 58.2% for GPT-6 Astra. A 7.5-point lead, and the benchmark chosen for the top of the announcement.

• BenchCAD with tools — 92.7% for Kimi K3.1 Pro against 95.9% for GPT-6 Astra. Astra wins by 3.2 points on the 3D-modelling benchmark that sits in the middle of Kimi's own pitch.

• Kimi Hallu Bench Hard — 8.3% claim-level hallucination for Kimi K3.1 Pro with tools against 5.5% for GPT-6 Astra; without tools, 5.4% against 4.2%. Astra is the more factually reliable model on this set by a margin that matters for research and document work.

That pattern — one large win, two smaller losses — is what a vendor table looks like when it is honest enough to include the rows it loses. The selection effect is in the headline, not in the data.

Access is a specification too

GPT-6 Astra is the first OpenAI model rated "critical" under the company's own Preparedness Framework, because it can independently find novel security flaws. Its rollout was staged: early-access partners first, then subscription tiers, then the API. Some capabilities are restricted by risk rating, and the model is sold as a gated frontier product rather than a utility.

Kimi K3.1 Pro takes the opposite approach and pays a different price for it. Moonshot removed cyber-related data from mid-training and from reinforcement learning, then published the cost: the shipped model scores 81.0% on xdev-bench-hard against 90.5% for a checkpoint trained without those measures. The company's stated principle is not to release open weights that extend the frontier of offensive cyber capability, and it plans to raise the ceiling gradually. The full weights are promised by 4 November 2026; until then the model is API-only despite being described as open-weight.

For a regulated buyer, the trade is legible. Astra gives you the strongest gated capability and an approval process. Kimi gives you a downloadable model in eleven days, a published capability reduction, and no gating beyond a standard API key.

Latency, as measured on our own route

Over the last seven days on OrcaRouter, GPT-6 Astra has a median time to first token of 1.64 seconds; Kimi K3.1 Pro, added to the catalogue on 24 October, has 7.15 seconds. On output speed the independent evaluation puts Kimi ahead — 73.5 tokens per second against 58.1 — so the two models differ in shape rather than in overall responsiveness: Astra starts faster, Kimi writes faster once it starts. For an interactive coding session the first number dominates the experience; for a long generation the second one does.

What to run, and when

Both models are callable today on one OrcaRouter key at their provider's list price with no markup added — $2.00 and $10.00 for Kimi K3.1 Pro, $10.00 and $50.00 for GPT-6 Astra — and both expose the same three request formats, so a routing policy can move traffic between them without touching application code.

The split that fits the evidence: send long-horizon terminal and agentic coding to Kimi K3.1 Pro, where it leads on the benchmark both labs run and where the 943,718-token output ceiling removes a client-side constraint. Send research, document verification and 3D or CAD work to GPT-6 Astra, where the hallucination and BenchCAD rows favour it. At a five-times price difference, a workload that spends 80% of its tokens on the first category and 20% on the second still cuts its bill by roughly two-thirds, with automatic failover covering whichever side stalls.

The one thing not to do is migrate on the strength of a single benchmark row. The vendor's 7.5-point lead on Terminal-Bench is real, and so are the two rows where it loses; the independent index says the two models are peers, and the per-task cost says the peer is 35% cheaper, not 80%. That is a strong case for routing by task — and a weak case for a wholesale switch.