GPT-5.3 Codex vs Muse Spark 1.1: which should you choose in 2026?
GPT-5.3 Codex (by OpenAI) and Muse Spark 1.1 (by Meta) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
No protocol-matched benchmark supports a controlled winner. Looking only at published point scores, GPT-5.3 Codex is higher on 0 benchmarks, while Muse Spark 1.1 is higher on 2 benchmarks (OSWorld-Verified, SWE-Bench Pro).
The evaluation setups are not fully aligned, so these are not controlled head-to-head wins.
Muse Spark 1.1 is about 2.4× cheaper on a blended 3:1 input/output basis ($2.00 vs $4.81 per 1M tokens).
Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 400,000 tokens).
Muse Spark 1.1 was released on Jul 9, 2026, about 5 months after GPT-5.3 Codex.
Choose GPT-5.3 Codex if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
Choose Muse Spark 1.1 if…
- • lower blended API cost matters for your workload
- • you work with longer documents, transcripts, or codebases
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | GPT-5.3 Codex | Muse Spark 1.1 |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | 42.8% Artificial Analysis |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | 77.3% OpenAI report | — |
Finance Agent v2 A Vals AI benchmark with 927 expert-reviewed questions modeling the work of entry-level financial analysts. Task score | — | 57.2% Vals AI |
JobBench A professional tool-use benchmark with 65 tasks spanning 35 white-collar occupations. Task score | — | 54.7% JobBench |
MCP Atlas A scaled tool-use benchmark with 1,000 multi-step tasks across 36 real MCP servers and 220 tools. Tasks completed | — | 88.1%±1.9 Scale AI |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward Protocols differ or are incompletely disclosed; not counted as a controlled win | 74.0% OpenAI report | 80.8% Meta report |
A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests. Pass@1 | — | 53.0%±3.0 DataCurve |
A contamination-resistant software-engineering benchmark with long-horizon tasks across multiple programming languages. Public and private splits are distinct protocols. Resolved Protocols differ or are incompletely disclosed; not counted as a controlled win | 56.8% OpenAI report | 61.5%±3.1 Scale AI |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
The individual-contributor Diamond subset of SWE-Lancer, which evaluates economically valuable real-world software-engineering tasks. Tasks completed | 81.4% OpenAI report | — |
Version 2.0 of the benchmark for completing realistic tasks in terminal environments. Results must not be merged with Terminal-Bench 2.1. Pass rate | 77.3% OpenAI report | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success Protocols differ or are incompletely disclosed; not counted as a controlled win | 79.1% Terminal-Bench | 80.0% Meta report |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | 92.6% OpenAI report | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | 62.1% Meta report |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | — | 75.3% LiveBench |
BabyVision A 388-question visual-understanding benchmark covering fine-grained discrimination, spatial perception, tracking, and pattern recognition. Accuracy | — | 76.3% Meta report |
The chart-reasoning portion of CharXiv, evaluating visual and mathematical reasoning over scientific figures. Accuracy | — | 88.4% Meta report |
Cybersecurity CTFs Capture-the-flag challenges used to evaluate an agent's practical cybersecurity task performance. Challenges completed | 77.6% OpenAI report | — |
| Arena preference scores | ||
Human preference score | — | 1487 ±6Preliminary |
Human preference score for code and web development | — | 1536 ±10Preliminary |
Benchmark sources
- Arena AI leaderboard
- AutomationBench-AA: Agentic SaaS Workflow Benchmark
- Introducing GPT-5.4
- Finance Agent v2 Leaderboard
- JobBench Leaderboard
- MCP Atlas Leaderboard
- Muse Spark 1.1 Evaluation Report
- DeepSWE 1.1 Leaderboard
- Introducing GPT-5.3-Codex
- SWE-Bench Pro Public Leaderboard
- Terminal-Bench 2.1 Verified Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 9, 2026.
Pricing, capabilities, and model facts
| Feature | GPT-5.3 Codex | Muse Spark 1.1 |
|---|---|---|
| Context & model facts | ||
| Developer | OpenAI | Meta |
| API provider | OpenAI | Meta |
| Input context | 400,000 tokens | 1,048,576 tokens |
| Maximum output | 128,000 tokens | 131,072 tokens |
| Released | Feb 5, 2026 | Jul 9, 2026 |
| Added to Writingmate | Feb 24, 2026 | Jul 16, 2026 |
| License | Proprietary | Proprietary |
| Knowledge cutoff | Not disclosed | Not disclosed |
| Capabilities | ||
| Inputs | Text, Image, File | Text, Image, Video, File, Audio |
| Outputs | Text | Text |
| Tool use | Yes | Yes |
| Reasoning | Yes | Yes |
| Vision | Yes | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $1.75 | $1.25 |
| Output (per 1M tokens) | $14.00 | $4.25 |
| Blended 3:1 input/output | $4.81 | $2.00 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Introducing GPT-5.3-Codex
- Muse Spark 1.1 Evaluation Report
- Meta Model API provider documentation
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jul 16, 2026.
Why Pay for Multiple Subscriptions?
Comparing GPT-5.3 Codex from OpenAI with Muse Spark 1.1 from Meta? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | GPT-5.3 Codex | Muse Spark 1.1 | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
GPT-5.3 Codex vs Muse Spark 1.1 FAQ
Which is better, GPT-5.3 Codex or Muse Spark 1.1?
No protocol-matched benchmark supports a controlled winner. Looking only at published point scores, GPT-5.3 Codex is higher on 0 benchmarks, while Muse Spark 1.1 is higher on 2 benchmarks (OSWorld-Verified, SWE-Bench Pro). The evaluation setups are not fully aligned, so these are not controlled head-to-head wins. Muse Spark 1.1 is about 2.4× cheaper on a blended 3:1 input/output basis ($2.00 vs $4.81 per 1M tokens). Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 400,000 tokens). Muse Spark 1.1 was released on Jul 9, 2026, about 5 months after GPT-5.3 Codex.
Which model is cheaper to use through an API?
Muse Spark 1.1 is about 2.4× cheaper on a blended 3:1 input/output basis ($2.00 vs $4.81 per 1M tokens).
Which model supports more context?
Muse Spark 1.1 has the larger context window (1,048,576 tokens vs 400,000 tokens).
Can I switch between GPT-5.3 Codex and Muse Spark 1.1?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.