GPT-5.3-Codex vs Llama 4 Maverick 17B Instruct FP8
GPT-5.3-Codex (2026) and Llama 4 Maverick 17B Instruct FP8 (2025) compare a coding-specialized model against a standalone API model. GPT-5.3-Codex ships a 400k-token context window, while Llama 4 Maverick 17B Instruct FP8 ships a 1m-token context window. On τ-bench, GPT-5.3-Codex leads by 9.3 pts. On pricing, Llama 4 Maverick 17B Instruct FP8 costs $0.15/1M input tokens versus $1.75/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GPT-5.3-Codex is coding-specialized model, while Llama 4 Maverick 17B Instruct FP8 is standalone API model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Product type | Coding-specialized model | Standalone API model |
| Best for | custom coding agents, code generation, and tool loops | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 400k | 1m |
| Cheapest output | $14/1M tokens | $0.60/1M tokens |
| Provider routes | 3 tracked | 10 tracked |
| Shared benchmarks | τ-bench leader | 1 rows |
Decision tradeoffs
- GPT-5.3-Codex holds a shared-benchmark lead on τ-bench, ahead by 9.3 points.
- GPT-5.3-Codex uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Llama 4 Maverick 17B Instruct FP8 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Maverick 17B Instruct FP8 has the lower cheapest tracked output price at $0.60/1M tokens.
- Llama 4 Maverick 17B Instruct FP8 has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 4 Maverick 17B Instruct FP8 uniquely exposes Multimodal in local model data.
- Local decision data tags Llama 4 Maverick 17B Instruct FP8 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route/tier: OpenRouter
Llama 4 Maverick 17B Instruct FP8
$270
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $4,630. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- Llama 4 Maverick 17B Instruct FP8 is $13.40/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Llama 4 Maverick 17B Instruct FP8 adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.3-Codex is $13.40/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-04-05 |
| Context window | 400k | 1m |
| Parameters | — | 400B (17B active) |
| Architecture | decoder only | mixture of experts |
| License | Proprietary | Llama 4 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use with conditions | Commercial use with conditions |
| Knowledge cutoff | 2025-08 | 2024-08 |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Input price | $1.75/1M tokens | $0.15/1M tokens |
| Output price | $14/1M tokens | $0.60/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.3-Codex | Llama 4 Maverick 17B Instruct FP8 |
|---|---|---|
| τ-bench | 77.8 | 68.5 |
Deep dive
On shared benchmark coverage, τ-bench has GPT-5.3-Codex at 77.8 and Llama 4 Maverick 17B Instruct FP8 at 68.5, with GPT-5.3-Codex ahead by 9.3 points. The largest visible gap is 9.3 points on τ-bench, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on multimodal input: Llama 4 Maverick 17B Instruct FP8, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share vision and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens on the cheapest tracked provider, while Llama 4 Maverick 17B Instruct FP8 lists $0.15/1M input and $0.60/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Maverick 17B Instruct FP8 lower by about $5.14 per million blended tokens. Availability is 3 providers versus 10, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support are central to the workload. Choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, GPT-5.3-Codex or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 supports 1m tokens, while GPT-5.3-Codex supports 400k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GPT-5.3-Codex or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Llama 4 Maverick 17B Instruct FP8 costs $0.15/1M input and $0.60/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Llama 4 Maverick 17B Instruct FP8 open source?
GPT-5.3-Codex is listed under Proprietary. Llama 4 Maverick 17B Instruct FP8 is listed under Llama 4 Community. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GPT-5.3-Codex or Llama 4 Maverick 17B Instruct FP8?
Both GPT-5.3-Codex and Llama 4 Maverick 17B Instruct FP8 expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, GPT-5.3-Codex or Llama 4 Maverick 17B Instruct FP8?
Llama 4 Maverick 17B Instruct FP8 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-5.3-Codex and Llama 4 Maverick 17B Instruct FP8?
GPT-5.3-Codex is available on OpenRouter, OpenAI API, and Vercel AI Gateway. Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-07. Data sourced from public model cards and provider documentation.