GLM 5.1 vs GPT-5.3-Codex Comparison: Benchmarks, Pricing & Speed (July 2026)

On price, GLM 5.1 is the cheaper option — about 20% below GPT-5.3-Codex on input tokens. For latency-sensitive workloads, GPT-5.3-Codex returns the first token sooner. Pick GLM 5.1 to minimise cost, or GPT-5.3-Codex when response speed matters most.

Both GLM 5.1 and GPT-5.3-Codex are available through the same OrcaRouter endpoint at provider cost with zero token markup, so switching between them is a one-line change and the numbers below are what you actually pay. This comparison pulls live pricing, the published context window, and OrcaRouter's own latency and throughput measurements so you can weigh cost against performance for your specific workload rather than relying on a vendor's headline benchmark. The right choice almost always depends on the shape of your traffic — prompt length, how much text you generate, how latency-sensitive your users are, and how hard the reasoning is — so the sections below break the decision down one dimension at a time and end with a concrete recommendation. Wherever a metric is missing for one of the two models, that row is left out rather than guessed, so every claim here is backed by a real number.

On input tokens GLM 5.1 costs $1.40 per 1M versus $1.75 for GPT-5.3-Codex, and on output $4.40 versus $14.00 per 1M. Output tokens are usually where the bill is decided: a chat or agent workload that generates long completions is dominated by the output rate, so the model that looks cheaper on input can still be the more expensive choice end to end. Estimate your real input-to-output ratio before picking on price alone — a retrieval-heavy prompt with a short answer and a short prompt with a long generation land on opposite sides of this table. A practical way to size this is to take a representative sample of your prompts, count the average input and output tokens, and multiply each by the two models' respective rates; the model with the lower blended cost on your actual mix is the one to beat. Remember that both prices here are the raw provider rate — OrcaRouter adds no markup — so the comparison is apples-to-apples and the savings you compute are the savings you keep.

GLM 5.1 accepts up to 200K tokens of context and GPT-5.3-Codex accepts 400K. The context window caps how much source material — documents, code, prior conversation — you can send in a single request. A larger window lets you skip chunking and retrieval plumbing for long inputs, but you still pay input-token rates for everything you send, so a bigger window is a capability, not a discount. Match the window to the longest single request your workload realistically produces rather than the largest number on the page. Also keep in mind that quality can degrade toward the end of a very long context on any model, so a large window is best treated as headroom for occasional long inputs rather than a licence to stuff every request to the limit.

Latency and throughput decide how the model feels in production. Median (p50) response latency is how long a typical request waits before the first token; throughput (tokens per second) sets how fast the answer streams once it starts. For interactive chat and agent loops, low p50 latency matters most because the user is waiting on the first token; for batch generation and long-form output, throughput dominates the wall-clock time because the answer is long. The 7-day trend charts above show whether each model's latency is stable or drifting, which a single headline number hides — a model with a great average but a noisy tail can still miss a strict p95 SLA. If your product has a latency budget, read both the median and the shape of the curve, and remember that end-to-end latency also includes your network hop and any retrieval or tool calls you make around the model.

Across the last 7 days, GPT-5.3-Codex holds the lower median response latency.

Benchmark scores approximate capability but are not a substitute for testing on your own prompts. The composite indices shown here aggregate multiple public evaluations, and the percentile marks where each model lands against every comparable model in the catalog — a useful shortlist signal, not a guarantee for your task. A model that leads on a general intelligence index can still trail on your domain (coding, extraction, multilingual, long-context reasoning), so use the benchmarks to narrow the field, then run both models on a representative slice of your traffic. Pay attention to the specific index that matches your use case rather than the top-line number: a coding-heavy product should weight the coding index, a research assistant the reasoning index. Benchmarks also age as models are updated, so treat them as a starting hypothesis you confirm with your own evaluation set.

If cost is the binding constraint, start with the cheaper model on your actual input-to-output mix and only move up if quality misses. If responsiveness is the priority — user-facing chat, agents, anything where someone is waiting — weight p50 latency and throughput over a small price gap. If you are pushing the hardest reasoning, coding, or long-context work, let the benchmark and context-window winner lead and accept the higher rate where it pays for itself. Because both models sit behind the same API, the low-risk move is to route a fraction of real traffic to each and compare cost, latency, and answer quality on your own prompts before committing. A common pattern is to tier: send the bulk of easy, high-volume requests to the cheaper or faster model and reserve the stronger model for the requests that actually need it, which captures most of the quality upside at a fraction of the cost. Whichever you choose, keep the switch reversible — with a one-line model-name change you can move traffic back the moment the numbers or your requirements shift.