Codex 5.3 vs Opus 4.6: Benchmarks, Speed, and Pricing Compared (March 2026)

Summary

Both models dropped on February 5, 2026. The simultaneous launch forced a direct comparison that neither company could control. A month later, the picture is clear: Codex 5.3 is the faster, cheaper, more token-efficient model. Opus 4.6 is the more accurate, deeper-reasoning model with a larger context window. Neither is universally better. The question is which bottleneck you hit first: speed or accuracy.

Stat Comparison

How these models perform across the dimensions that affect real workflows, rated on a 5-bar scale.

Benchmark Deep Dive

Four benchmarks give a useful cross-section of coding ability. Each tests something different, and each tells a different story about these models.

SWE-bench: The Contamination Problem

OpenAI stopped reporting SWE-bench Verified scores after their audit found that every frontier model (GPT-5.2, Claude Opus 4.5, Gemini 3 Flash) could reproduce verbatim gold patches for certain tasks. Training data contamination means Verified scores are inflated across the board, not just for one model.

SWE-bench Pro was created as the cleaner alternative. On Pro, Codex 5.3 leads at 56.8% vs Opus 4.6 at 55.4%. The gap is 1.4 percentage points in Codex's favor. Small, but consistent with Codex's execution-oriented strengths.

Terminal-Bench 2.0: Where Codex Dominates

Terminal-Bench 2.0 tests agents in realistic terminal environments: compiling code, training models, configuring servers, playing games, debugging systems. Codex 5.3 scores 77.3%, a 12.6-point jump from GPT-5.2's 64.7%. Opus 4.6 scores 65.4%.

The 11.9-point gap on Terminal-Bench is the largest benchmark delta between these models. If your work is terminal-native (DevOps, scripting, CLI tooling, infrastructure automation), Codex has a measurable, reproducible advantage.

HumanEval: Saturated

Codex 5.3 scores 98.1%. Opus 4.6 scores 97.6%. Both have effectively solved HumanEval. The 0.5% gap is within noise. This benchmark no longer differentiates frontier models.

Speed Comparison

Speed is measured three ways: output tokens per second, time to first token (TTFT), and total task completion time. Each metric tells a different part of the story.

Codex-Spark: 1,000+ Tokens Per Second

OpenAI launched GPT-5.3-Codex-Spark on February 12, 2026. It runs on Cerebras WSE-3 wafer-scale chips, not Nvidia GPUs. This is OpenAI's first production workload on non-Nvidia hardware. The model is distilled from Codex 5.3, with a 128K context window (half the standard 256K), trading reasoning depth for raw throughput.

At 1,000+ tok/s, Spark is 15x faster than standard Codex and 21x faster than standard Opus. Combined with WebSocket optimizations that cut per-token overhead by 30% and time-to-first-token by 50%, it delivers near-instant code generation for interactive use.

Opus 4.6: The Thinking Pause Trade-off

Opus 4.6 introduced a "thinking pause" where the model generates hidden reasoning traces before streaming visible text. This pushes time-to-first-token to 7.83 seconds on average (vs median 2.60s for models in the same price tier, per Artificial Analysis). The delay buys accuracy: those reasoning traces are why Opus scores higher on SWE-bench Pro.

Anthropic's Fast Mode runs the same model under speed-prioritized inference at 2.5x higher output tokens per second, roughly 115 tok/s. The trade-off: 6x premium pricing ($30/$150 per million tokens).

Pricing Comparison

Raw per-token pricing tells half the story. Token consumption per task tells the other half.

Effective Cost: Tokens Per Task

Opus is 2.5x more expensive per token. But Opus also uses 2-4x more tokens per task. In benchmark testing, a Figma plugin build consumed 1.5M tokens on Codex vs 6.2M on Opus, a 4.2x difference. A scheduler app: 73K tokens on Codex vs 235K on Opus, 3.2x.

For a concrete example: generating 1M output tokens costs $10 on Codex and $25 on Opus. But if Opus needs 3x the tokens to complete the same task, the effective cost is $75 vs $10. That 7.5x gap matters at scale.

The counter-argument: Opus's extra tokens buy higher first-pass accuracy, which means fewer retry cycles. If Codex requires 3 attempts to get a complex refactoring right while Opus nails it in 1, the cost equation flips. The break-even depends on task complexity.

Subscription Pricing

Architecture Differences

These models are built differently, served differently, and optimized for different workloads. Understanding the architecture explains why the benchmarks look the way they do.

Codex: Speed Through Hardware Diversification

OpenAI's deployment of Codex-Spark on Cerebras WSE-3 chips is architecturally significant. Cerebras's wafer-scale engine runs the entire model on a single chip, eliminating the inter-chip communication overhead that limits token throughput on GPU clusters. The 80% reduction in client/server roundtrip overhead and 30% per-token overhead reduction come from WebSocket optimizations in the Responses API, not the model itself.

Diff-based forgetting is Codex's novel approach to memory management. Instead of compacting old context into summaries (which loses structural relationships), stale context is diffed away, keeping only the delta. This preserves more of the codebase's structural understanding across long sessions.

Opus: Depth Through Thinking Traces

Opus 4.6's hidden reasoning traces are the architectural choice that explains both its accuracy advantage and its speed penalty. The model "thinks" before responding, generating internal reasoning that never appears in the visible output. This is why TTFT is 7.83 seconds on average: the model is solving the problem before writing the answer.

The 1M token context window (beta) is the other differentiator. For codebases where understanding requires reading 500+ files, Opus can hold the entire project in context. Codex's 256K limit means it has to rely more on selective file reading and search, which works for targeted tasks but limits holistic understanding.

When to Use Opus 4.6

When to Use Codex 5.3

"Codex explores like a new hire wanting to understand the system before the first commit. Opus moves fast when it knows patterns, but improvises when it doesn't."

How Morph Routes Between Them

Choosing one model for all tasks leaves performance on the table. The optimal approach routes each task to the model that handles it best. This is what Morph does.

The Routing Problem

If you route everything to Opus 4.6, you overpay by 6-10x on tasks that Codex handles equally well. If you route everything to Codex 5.3, you get lower accuracy on complex refactoring where Opus's reasoning depth matters. Most teams find that 70-80% of their coding tasks are "execution tasks" (implement this spec, write this test, fix this bug) where Codex's speed and token efficiency win. The remaining 20-30% are "reasoning tasks" (redesign this architecture, debug this race condition, refactor across 50 files) where Opus's depth wins.

# Morph routes to the right model automatically
# Simple implementation task → Codex 5.3 (fast, cheap)
response = client.chat.completions.create(
    model="morph-v3-fast",  # Morph picks the model
    messages=[{"role": "user", "content": "Add pagination to /api/users"}]
)

# Complex reasoning task → Opus 4.6 (accurate, thorough)
response = client.chat.completions.create(
    model="morph-v3-fast",
    messages=[{"role": "user", "content": "Refactor auth module from sessions to JWT across 30 files, preserving backward compatibility"}]
)

# Same API. Morph detects task complexity and routes accordingly.
# Result: Codex-level speed on simple tasks, Opus-level accuracy on hard ones.

WarpGrep + Opus: 57.5% SWE-bench Pro

Morph's WarpGrep v2 codebase search tool pushed Opus 4.6 from 55.4% to 57.5% on SWE-bench Pro, a 2.1-point improvement. Better search means the model spends fewer tokens reading irrelevant files and more tokens reasoning about the problem. WarpGrep works as an MCP server, compatible with Claude Code, Codex, Cursor, and any tool that supports MCP.

Frequently Asked Questions

Is Codex 5.3 or Opus 4.6 better for coding?

Codex 5.3 leads on execution benchmarks: 77.3% Terminal-Bench 2.0, 98.1% HumanEval, 64.7% OSWorld-Verified. Opus 4.6 leads SWE-bench Verified at 80.8% and scores 55.4% on SWE-bench Pro. For terminal workflows and fast iteration, Codex wins. For complex multi-file reasoning and large codebases, Opus wins.

How much does Codex 5.3 cost vs Opus 4.6?

Codex 5.3: $2 input / $10 output per million tokens. Opus 4.6: $5 input / $25 output per million tokens. Opus is 2.5x more expensive per token, and uses 2-4x more tokens per task. Effective cost difference is 6-10x for typical workloads. Opus offers a 50% batch API discount and 90% prompt caching discount.

How fast is Codex 5.3 vs Opus 4.6?

Standard Codex 5.3: 65-70 tok/s. Standard Opus 4.6: 46 tok/s. Codex-Spark on Cerebras: 1,000+ tok/s. Opus Fast Mode: ~115 tok/s at 6x price premium. Codex is 1.4-1.5x faster at standard tiers, and Spark is 8.7x faster than Opus Fast Mode.

What is GPT-5.3-Codex-Spark?

A distilled variant of Codex 5.3, running on Cerebras WSE-3 wafer-scale hardware at 1,000+ tok/s. It uses a 128K context window (vs 256K standard) and trades some reasoning depth for speed. Launched February 12, 2026. OpenAI's first production deployment on non-Nvidia hardware.

What is Opus 4.6's context window?

200K tokens by default, with a 1M token context window in beta. The extended context uses premium pricing: $10 input / $37.50 output per million tokens for requests exceeding 200K. Codex 5.3's context is 256K tokens standard, 128K for Spark.

Which model is better for SWE-bench?

Opus 4.6 leads SWE-bench Verified at 80.8%. On SWE-bench Pro, Codex 5.3 scores 56.8% vs Opus at 55.4%. Codex does not report Verified scores due to contamination concerns. On the apples-to-apples SWE-bench Pro comparison, Codex leads by 1.4 points.

Which model wins Terminal-Bench 2.0?

Codex 5.3 at 77.3%, vs Opus 4.6 at 65.4%. The 11.9-point gap is the largest benchmark delta between these models. Terminal-Bench tests real terminal agent tasks developed by Stanford and the Laude Institute.

Can I use both models together?

Yes. Many teams route tasks by type: Codex for fast implementation, code review, and terminal tasks; Opus for complex reasoning, multi-file refactoring, and architectural decisions. Morph's API does this routing automatically based on task complexity signals.

What are the HumanEval scores?

Codex 5.3: 98.1%. Opus 4.6: 97.6%. Both have effectively saturated the benchmark. The 0.5% difference is within measurement noise. More challenging benchmarks like SWE-bench Pro and Terminal-Bench show meaningful gaps.

Which model uses fewer tokens?

Codex 5.3 uses 2-4x fewer output tokens on equivalent tasks. Opus 4.6 generates more tokens because it includes extended reasoning traces. Codex optimizes for efficiency; Opus optimizes for thoroughness. On easy tasks, Codex's efficiency saves money. On hard tasks, Opus's thoroughness saves retry cycles.