Accounts Fireworks Models DeepSeek V3p2 vs GPT-3.5 Turbo
Accounts Fireworks Models DeepSeek V3p2 (Fireworks AI, 163,840-token context) versus GPT-3.5 Turbo (Azure OpenAI, 4,097-token context). GPT-3.5 Turbo is cheaper by 11% on a blended token mix. Accounts Fireworks Models DeepSeek V3p2 uniquely supports structured output (json schema) and native reasoning mode. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — Accounts Fireworks Models DeepSeek V3p2 vs GPT-3.5 Turbo
Accounts Fireworks Models DeepSeek V3p2 and GPT-3.5 Turbo target overlapping workloads but differ sharply on economics. GPT-3.5 Turbo runs roughly 11% cheaper on a blended input-plus-output token mix, which translates to approximately $288 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Accounts Fireworks Models DeepSeek V3p2 ships a 163,840-token context window, 40.0x larger than GPT-3.5 Turbo's 4,097 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 4,097 tokens, the extra context on Accounts Fireworks Models DeepSeek V3p2 is insurance you may never use — and GPT-3.5 Turbo may win on other axes.
On capability surface area, the models diverge: Accounts Fireworks Models DeepSeek V3p2 supports structured output (json schema) where the other does not; Accounts Fireworks Models DeepSeek V3p2 supports native reasoning mode where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: gpt-3-5-turbo
provider: azure-openai
fallback:
model: accounts-fireworks-models-deepseek-v3p2
provider: fireworks-ai
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| Accounts Fireworks Models DeepSeek V3p2 | GPT-3.5 Turbo | |
|---|---|---|
| Input price | $0.560/M | $0.500/M |
| Output price | $1.68/M | $1.50/M |
| Context window | 163,840 | 4,097 |
| Max output | 163,840 | 4,096 |
| Function calling | ✓ | ✓ |
| Vision | — | — |
| Audio input | — | — |
| Reasoning | ✓ | — |
| Prompt caching | — | — |
| Structured output | ✓ | — |
| Pricing verified | Aug 6, 2026 | Aug 6, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | Accounts Fireworks Models DeepSeek V3p2 | GPT-3.5 Turbo | Delta |
|---|---|---|---|
| Startup 10K requests/day | $269 /mo | $240 /mo | $28.80/mo |
| Mid-market 100K requests/day | $2,688 /mo | $2,400 /mo | $288/mo |
| Enterprise 1M requests/day | $26,880 /mo | $24,000 /mo | $2,880/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
Your workload needs long context — Accounts Fireworks Models DeepSeek V3p2 fits 163,840 tokens versus the other model's 4,097, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Your tasks involve multi-step planning or math-heavy reasoning — Accounts Fireworks Models DeepSeek V3p2 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
Capability diff — what you gain and lose on the swap
A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Accounts Fireworks Models DeepSeek V3p2, switching to GPT-3.5 Turbo means re-architecting that path (and vice versa).
- • Structured output (JSON schema)
- • Native reasoning mode
Capabilities both share (2)
- ✓ Function calling
- ✓ Streaming
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes down 97% when moving from Accounts Fireworks Models DeepSeek V3p2 (163,840) to GPT-3.5 Turbo (4,097). Re-check any prompt that relies on cramming long history or documents.
- Max output tokens differ: 163,840 on Accounts Fireworks Models DeepSeek V3p2 vs 4,096 on GPT-3.5 Turbo. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
- Accounts Fireworks Models DeepSeek V3p2 has capabilities GPT-3.5 Turbo lacks: Structured output (JSON schema), Native reasoning mode. Switching to GPT-3.5 Turbo means re-architecting any flow that depends on these.
- Provider changes from Fireworks AI to Azure OpenAI. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test Accounts Fireworks Models DeepSeek V3p2 vs GPT-3.5 Turbo in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark Accounts Fireworks Models DeepSeek V3p2 primary, mirror 20% of traffic to GPT-3.5 Turbo in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — Accounts Fireworks Models DeepSeek V3p2 vs GPT-3.5 Turbo
Which is cheaper, Accounts Fireworks Models DeepSeek V3p2 or GPT-3.5 Turbo? ▾
GPT-3.5 Turbo is cheaper by roughly 11% on a blended input + output token mix. Input prices are $0.560/M for Accounts Fireworks Models DeepSeek V3p2 versus $0.500/M for GPT-3.5 Turbo; output prices are $1.68/M versus $1.50/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of Accounts Fireworks Models DeepSeek V3p2 versus GPT-3.5 Turbo? ▾
Accounts Fireworks Models DeepSeek V3p2 supports up to 163,840 tokens of context. GPT-3.5 Turbo supports up to 4,097 tokens. Accounts Fireworks Models DeepSeek V3p2 has the larger window by a factor of 40.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do Accounts Fireworks Models DeepSeek V3p2 and GPT-3.5 Turbo both support tool calling? ▾
Yes — both Accounts Fireworks Models DeepSeek V3p2 and GPT-3.5 Turbo support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
When should I choose Accounts Fireworks Models DeepSeek V3p2 over GPT-3.5 Turbo? ▾
Your workload needs long context — Accounts Fireworks Models DeepSeek V3p2 fits 163,840 tokens versus the other model's 4,097, enough headroom for full books, large codebases, or 100+ page documents in one shot. Your tasks involve multi-step planning or math-heavy reasoning — Accounts Fireworks Models DeepSeek V3p2 ships a native reasoning mode that explicitly thinks before responding, the other doesn't.
When should I choose GPT-3.5 Turbo over Accounts Fireworks Models DeepSeek V3p2? ▾
On the data this page surfaces, GPT-3.5 Turbo is the right pick when Accounts Fireworks Models DeepSeek V3p2's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
How do I A/B test Accounts Fireworks Models DeepSeek V3p2 against GPT-3.5 Turbo in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.