GPT-5.3-Codex-Spark and the Cerebras Inference Stack: Real-Time Coding at 1,000 Tokens per Second

GPT-5.3-Codex-Spark is OpenAI’s first model purpose-built for real-time coding iteration, and the first production model served entirely on non-NVIDIA hardware. Released on 12 February 2026 as a research preview for ChatGPT Pro subscribers ¹, Spark trades reasoning depth for raw speed — delivering over 1,000 tokens per second on the Cerebras Wafer-Scale Engine 3 (WSE-3) ². For Codex CLI users, it changes the economics of interactive workflows: rapid prototyping, single-file edits, and frontend iteration become near-instantaneous.

This article covers the hardware underpinnings, the infrastructure optimisations OpenAI shipped alongside Spark, how to configure it in Codex CLI, benchmark realities, and practical patterns for integrating it into a multi-model workflow.

The Cerebras WSE-3 and Why It Matters

Codex-Spark is the first fruit of OpenAI’s multi-year, $10 billion+ agreement with Cerebras Systems ³. The WSE-3 is Cerebras’ third-generation wafer-scale chip, packing 4 trillion transistors and the largest on-chip memory of any AI accelerator ². The architecture scales out to thousands of systems, pushing fast memory capacity into the multi-terabyte range ².

For coding workloads, the key advantage is latency, not throughput. Traditional GPU clusters optimise for batch throughput; the WSE-3’s on-chip SRAM eliminates the memory-bandwidth bottleneck that causes per-token latency spikes in transformer inference. The result: Spark sustains ~1,000 tokens/s compared to GPT-5.3-Codex’s ~65–70 tokens/s — roughly a 15× speed-up ⁴.

graph LR
    subgraph "Traditional GPU Inference"
        A[Token Request] --> B[HBM Read]
        B --> C[Compute]
        C --> D[HBM Write]
        D --> E[Next Token]
    end

    subgraph "WSE-3 Inference"
        F[Token Request] --> G[On-Chip SRAM]
        G --> H[Compute + Emit]
    end

    style B fill:#f96,stroke:#333
    style D fill:#f96,stroke:#333
    style G fill:#6f9,stroke:#333

Infrastructure Optimisations

Spark’s raw speed would be wasted if the network stack added latency on every round-trip. OpenAI shipped three infrastructure changes alongside the model ⁵:

Optimisation	Improvement
Persistent WebSocket connection	80% reduction in client/server round-trip overhead
Responses API stream rewrite	30% reduction in per-token overhead
Session initialisation rewrite	50% improvement in time-to-first-token

The WebSocket transport is enabled by default for Spark and is rolling out as the default for all models ⁵. In config.toml, provider-level WebSocket support is exposed via the supports_websockets key ⁶:

[model_providers.openai]
supports_websockets = true

Configuring Spark in Codex CLI

Quick start

Launch a session with Spark directly:

codex -m gpt-5.3-codex-spark

Or switch mid-session using the /model command:

/model gpt-5.3-codex-spark

Persistent configuration

Set Spark as your default model in ~/.codex/config.toml:

model = "gpt-5.3-codex-spark"

Or scope it to a project by placing .codex/config.toml in the repository root ⁶.

Profile-based switching

For teams that want Spark for rapid iteration but the full model for deep work, profiles provide clean switching ⁶:

[profiles.spark]
model = "gpt-5.3-codex-spark"
model_reasoning_effort = "low"

[profiles.deep]
model = "gpt-5.3-codex"
model_reasoning_effort = "xhigh"

Launch with codex --profile spark for quick edits or codex --profile deep for complex refactors.

Access requirements

Spark is currently available only to ChatGPT Pro subscribers ($200/month) ¹. API access is rolling out to select design partners ². Users on Plus or Team plans will see the model listed in /model but cannot use it — the CLI silently routes to the default model ⁷. Check actual model availability with /status rather than /model ⁸.

Benchmark Reality Check

OpenAI’s announcement claims Spark matches GPT-5.3-Codex’s 77.3% on Terminal-Bench 2.0 ¹. Independent community benchmarks tell a different story:

Benchmark	GPT-5.3-Codex	Codex-Spark (OpenAI)	Codex-Spark (Independent)
SWE-Bench Pro	56.8% ⁹	~56% ⚠️	~56%
Terminal-Bench 2.0	77.3% ⁹	77.3% ¹	~58.4% ¹⁰
Context Window	400k+ tokens	128k tokens	128k tokens

The ~19 percentage point gap on Terminal-Bench 2.0 between official and independent scores is significant ¹⁰. Dominic Elm’s analysis notes: “Spark is a smaller, distilled model that trades intelligence for speed, scoring 58.4% on Terminal-Bench 2.0 versus the full Codex’s 77.3%” ¹⁰.

⚠️ OpenAI’s SWE-Bench Pro claim of “matching” accuracy may reflect a specific scaffold configuration; independent reproductions with standardised scaffolding show lower scores.

The practical takeaway: Spark is meaningfully more capable than GPT-5.1-Codex-mini (+12.3 points on Terminal-Bench) ¹⁰ but not equivalent to the full GPT-5.3-Codex on complex multi-step tasks. It reportedly drifts after 6–8 reasoning steps, whereas the full model maintains coherence across 12+ step plans ⁴.

When to Use Spark (and When Not To)

flowchart TD
    A[New Task] --> B{Single file edit?}
    B -->|Yes| C{Quick iteration?}
    C -->|Yes| D[Use Spark]
    B -->|No| E{Multi-step reasoning?}
    E -->|Yes| F[Use GPT-5.3-Codex or GPT-5.4]
    E -->|No| G{Security-critical?}
    G -->|Yes| F
    G -->|No| D
    C -->|No| F

    style D fill:#6f9,stroke:#333
    style F fill:#69f,stroke:#333

Spark excels at:

Rapid prototyping and boilerplate generation
Single-file edits (CSS, React components, configuration)
Frontend iteration cycles
Interactive pair programming where latency matters more than depth

Stick with the full model for:

Multi-step debugging across services
Security-critical code (authentication, encryption, validation)
Database migrations requiring stateful reasoning
Large codebase analysis beyond the 128k context window

Multi-Model Workflow: Spark as a Subagent

One powerful pattern combines Spark’s speed with a flagship model’s reasoning. Using Codex CLI’s multi-agent v2 system, you can delegate quick edits to a Spark-powered subagent while the primary agent handles orchestration:

# .codex/agents/quick-edit.toml
model = "gpt-5.3-codex-spark"
model_reasoning_effort = "low"
instructions = """
You handle single-file edits and quick formatting fixes.
Keep changes minimal and focused.
"""

The primary agent (running GPT-5.4 or GPT-5.3-Codex) can then spawn this subagent for rapid tasks while retaining the full model’s reasoning for architectural decisions.

API Pricing and Cost Considerations

For API access (rolling out to design partners), Spark’s pricing is significantly lower than the full model ¹¹:

Model	Input (per 1M tokens)	Output (per 1M tokens)
gpt-5.3-codex-spark	$1.75	$14.00

⚠️ API pricing is subject to change as Spark exits research preview.

Combined with its 15× throughput, Spark can be dramatically more cost-effective for high-volume, low-complexity coding tasks — particularly in CI/CD pipelines using codex exec for automated formatting, linting fixes, and boilerplate generation.

The Strategic Picture

Codex-Spark represents OpenAI’s first step toward a dual-mode Codex architecture: real-time collaboration for rapid iteration alongside longer-horizon reasoning for deep work ². Over time, these modes will blend — Codex maintaining an interactive loop while delegating complex work to background subagents ².

For the Codex CLI ecosystem, the implications are clear: model selection is no longer just about capability but about matching latency characteristics to task complexity. The profile system, multi-agent delegation, and the upcoming plugin marketplace all support this heterogeneous-model future.

The Cerebras partnership also signals OpenAI’s strategic diversification away from NVIDIA dependency ³. Whether WSE-3 inference scales to flagship models remains to be seen, but for distilled, speed-optimised variants like Spark, the wafer-scale approach delivers a qualitatively different developer experience.

Citations

OpenAI, “Introducing GPT-5.3-Codex-Spark,” 12 February 2026. https://openai.com/index/introducing-gpt-5-3-codex-spark/ ↩ ↩² ↩³ ↩⁴
Cerebras, “Introducing OpenAI GPT-5.3-Codex-Spark Powered by Cerebras,” February 2026. https://www.cerebras.ai/blog/openai-codexspark ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶
Tom’s Hardware, “OpenAI launches GPT-5.3-Codex-Spark on Cerebras chips — marks AI giant’s first production deployment away from Nvidia,” February 2026. https://www.tomshardware.com/tech-industry/artificial-intelligence/openai-lauches-gpt-53-codes-spark-on-cerebras-chips ↩ ↩²
Turing College, “Codex 5.3 vs. Codex Spark: Speed vs. Intelligence,” 2026. https://www.turingcollege.com/blog/codex-5-3-vs-codex-spark-speed-vs-intelligence ↩ ↩²
InfoQ, “OpenAI Codex-Spark Achieves Ultra-Fast Coding Speeds on Cerebras Hardware,” March 2026. https://www.infoq.com/news/2026/03/open-ai-codex-spark/ ↩ ↩²
OpenAI, “Configuration Reference – Codex,” 2026. https://developers.openai.com/codex/config-reference ↩ ↩² ↩³
GitHub, “Codex Spark not yet available? · Issue #11623,” 12 February 2026. https://github.com/openai/codex/issues/11623 ↩
GitHub, “Limitations of GPT-5.3-Codex-Spark visible in /status but not /model · Issue #12992,” 2026. https://github.com/openai/codex/issues/12992 ↩
OpenAI, “Introducing GPT-5.3-Codex,” 5 February 2026. https://openai.com/index/introducing-gpt-5-3-codex/ ↩ ↩²
Dominic Elm (@elmd_), “GPT-5.3-Codex-Spark is not GPT-5.3-Codex!,” X/Twitter, February 2026. https://x.com/elmd_/status/2023417837193240788 ↩ ↩² ↩³ ↩⁴
TypingMind, “Connect and use GPT-5.3 Codex Spark from OpenAI with API Key,” 2026. https://www.typingmind.com/guide/openai/gpt-5.3-codex-spark ↩